Storage node processing of data functions using overlapping symbols

ABSTRACT

Example storage systems, storage nodes, and methods provide storage node processing of data functions, such as map-reduce functions, using overlapping symbols. Storage nodes are configured to partition data units into symbols that include an overlap data portion of an adjacent symbol and erasure encode the symbols. The storage nodes may then decode erasure encoded symbols, identify subunits of a data unit from the decoded symbols, and process the subunits using map-functions to generate intermediate contexts. A reduce-function may be used to determine a function result using the intermediate contexts.

TECHNICAL FIELD

The present disclosure generally relates to data storage, and in a moreparticular example, to processing data functions across storage nodes.

BACKGROUND

Often, distributed storage systems are used to store large amounts(e.g., terabytes, petabytes, exabytes, etc.) of data, such as objects orfiles in a distributed and fault tolerant manner with a predeterminedlevel of redundancy.

Some existing object storage systems store data objects referenced by anobject identifier versus file systems. This can generally allow objectstorage systems to surpass the maximum limits for storage capacity offile systems in a flexible way such that, for example, storage capacitycan be added or removed as a function of the applications, systems,and/or enterprise needs, while reducing degradation in performance asthe system grows. As a result, object storage systems are often selectedfor large-scale storage systems.

These large-scale storage systems may support the storage of data thatis erasure coded and distributed across many storage devices. Data, suchas files or objects, may be split into messages or similar data unitswhich have an upper bound in size. These data units are then split upinto a number of symbols. The symbols are then used as input for erasurecoding. For example, when using a systematic erasure coding algorithm,the output of the erasure coding process yields the original symbols anda fixed number of additional parity symbols. The sum of these symbolsare distributed among a selection of storage devices.

When a client system wants to perform an operation on the data stored inthe system, all data may need to be reconstructed from the storedsymbols and sent to the client system for processing. In manyconfigurations, the aggregate bandwidth between storage system andclient is many times smaller than the aggregate bandwidth of the storagenodes inside the storage system. Therefore, in such configurations,moving the data to the client is very inefficient, particularly if theoutput of the processing is small relative to the amount of data beingprocessed, which is often the case.

For example, many systems may run operations called map-reduceoperations on large volumes of data. A map-reduce operation may becomposed of several functions. Each unit of data, such as an object orfile, is first processed through a map-function to yield a single resultfor each unit. Then the results, sometimes referred to as intermediatecontexts, are aggregated and combined (or reduced) using areduce-function to provide a single result for the entire data set. Insome configurations, when a client system wishes to execute operations,such as a map-reduce operation, on data stored in a distributed storagesystem, the data will first be fetched from the storage system, makingthe storage system reconstruct the original data, and then the clientsystem will execute its map-reduce operation on the fetched data set.

As large-scale storage systems scale, transfer of large data sets fordata operations may be inefficient. A need exists for at least storagesystems that execute data functions, such as map-reduce functions, withreduced data transfers and improved efficiency and reliability.

SUMMARY

Various aspects for data function processing by storage systems,particularly, map-reduce and similar functions executed by the storagenodes are described.

One general aspect includes a system that includes an encoding engineconfigured to partition at least one data unit into a plurality ofsymbols, where each symbol of the plurality of symbols includes anoverlap data portion of an adjacent symbol of the plurality of symbols,and erasure encode the plurality of symbols into a plurality of erasureencoded symbols. A plurality of storage nodes are configured to: storethe plurality of erasure encoded symbols distributed among the pluralityof storage nodes; decode the plurality of erasure encoded symbols intothe plurality of symbols for the at least one data unit; and process theplurality of symbols using a map-function to generate a plurality ofintermediate contexts. The system also includes a reduce-functionprocessor configured to determine, responsive to receiving the pluralityof intermediate contexts from the plurality of storage nodes, areduce-function result using the plurality of intermediate contexts.

Implementations may include one or more of the following features. Eachstorage node of the plurality of storage nodes may include at least onestorage medium configured to store at least one local erasure encodedsymbol of the plurality of erasure encoded symbols, and each storagenode of the plurality of storage nodes may be further configured to:read the at least one local erasure encoded symbol from the at least onestorage medium; decode the at least one local erasure encoded symbolinto a local symbol for the at least one data unit; and process thelocal symbol using the map-function to generate at least oneintermediate context of the plurality of intermediate contexts. Eachstorage node may be further configured to process the local symbol usingthe map-function in parallel with at least one other storage node of theplurality of storage nodes processing another local symbol using themap-function. The map-function may be configured to process targetsubunits within each symbol of the plurality of symbols to generate anintermediate context of the plurality of intermediate contexts. Targetsubunit lengths may be smaller than symbol lengths, resulting in anincomplete subunit portion of each symbol, and at least a portion of theincomplete subunit portion of each symbol may be included in the overlapdata portion of the adjacent symbol of the plurality of symbols. Theoverlap data portion may have a predetermined overlap length, a maximumtarget subunit length may be less than the predetermined overlap length,and a complete version of each target subunit of the target subunits maybe included in at least one symbol of the plurality of symbols. Thesystem may further include an incomplete target processor configured to:aggregate at least one complete target subunit from incomplete subunitportions, where the plurality of intermediate contexts includesincomplete subunit portions; process the at least one complete targetsubunit using the map-function to generate at least one additionalintermediate context; add the at least one additional intermediatecontext to the plurality of intermediate contexts. The reduce-functionprocessor may be further configured to assemble the plurality ofintermediate contexts into an ordered list of intermediate contexts. Theordered list of intermediate contexts may correspond to a symbol orderof the plurality of erasure encoded symbols and a terminal symbol in thesymbol order may not include the overlap data portion. The system mayfurther include a client request handler configured to: receive themap-function and the reduce-function; identify a map-reduce data setincluding the at least one data unit; return the reduce-function resultto a client system, where the client system is not among the pluralityof storage nodes. The client request handler may be further configuredto receive, from the client system, a predetermined overlap length forthe overlap data portion of each of the plurality of symbols.

Another general aspect includes a computer-implemented method thatincludes: partitioning at least one data unit into a plurality ofsymbols, where each symbol of the plurality of symbols includes anoverlap data portion of an adjacent symbol of the plurality of symbols;erasure encoding the plurality of symbols into a plurality of erasureencoded symbols; storing the plurality of erasure encoded symbolsdistributed among a plurality of storage nodes; decoding, at theplurality of storage nodes, the plurality of erasure encoded symbolsinto the plurality of symbols for the at least one data unit;processing, at the plurality of storage nodes, the plurality of symbolsusing a map-function to generate a plurality of intermediate contexts;and determining, responsive to receiving the plurality of intermediatecontexts from the plurality of storage nodes, a reduce-function resultusing the plurality of intermediate contexts.

Implementations may include one or more of the following features. Eachstorage node of the plurality of storage nodes may include at least onestorage medium configured to store at least one local erasure encodedsymbol of the plurality of erasure encoded symbols. Decoding theplurality of erasure encoded symbols may include decoding, at eachstorage node of the plurality of storage nodes, the at least one localerasure encoded symbol into a local symbol for the at least one dataunit. Processing the plurality of symbols may include processing, ateach storage node of the plurality of storage nodes, the local symbolusing the map-function to generate at least one intermediate context ofthe plurality of intermediate contexts. Processing, at each storage nodeof the plurality of storage nodes, the local symbol using themap-function may be executed in parallel with at least one other storagenode of the plurality of storage nodes processing another local symbolusing the map-function. Processing the plurality of symbols may includeprocessing target subunits within each symbol of the plurality ofsymbols to generate at least one intermediate context of the pluralityof intermediate contexts. The target subunits may have subunit lengthsthat are smaller than symbol lengths of the plurality of symbols,resulting in an incomplete subunit portion of each symbol and at least aportion of the incomplete subunit portion of each symbol may be includedin the overlap data portion of the adjacent symbol of the plurality ofsymbols. The overlap data portion may have a predetermined overlaplength, a maximum target subunit length may be less than thepredetermined overlap length, and a complete version of each targetsubunit of the target subunits may be included in at least one symbol ofthe plurality of symbols. The computer-implemented method may furtherinclude: aggregating at least one complete target subunit fromincomplete subunit portions, where the plurality of intermediatecontexts includes incomplete subunit portions; processing the at leastone complete target subunit using the map-function to generate at leastone additional intermediate context; and adding the at least oneadditional intermediate context to the plurality of intermediatecontexts. The computer-implemented method may further include assemblingthe plurality of intermediate contexts into an ordered list ofintermediate contexts, where the ordered list of intermediate contextscorresponds to a symbol order of the plurality of erasure encodedsymbols and a terminal symbol in the symbol order does not include theoverlap data portion. The computer-implemented method may furtherinclude: receiving the map-function and the reduce-function; identifyinga map-reduce data set including the at least one data unit; andreturning the reduce-function result to a client system. Thecomputer-implemented method further including: receiving, from theclient system, a predetermined overlap length for the overlap dataportion of each of the plurality of symbols.

Another general aspect includes a system that includes a plurality ofstorage nodes configured to store at least one data unit in a pluralityof erasure encoded symbols distributed among the plurality of storagenodes. Means are provided for partitioning the at least one data unitinto a plurality of symbols, where each symbol of the plurality ofsymbols includes an overlap data portion of an adjacent symbol of theplurality of symbols. Means are provided for erasure encoding theplurality of symbols into the plurality of erasure encoded symbols.Means are provided for storing the plurality of erasure encoded symbolsdistributed among the plurality of storage nodes. Means are provided fordecoding, at the plurality of storage nodes, the plurality of erasureencoded symbols into the plurality of symbols for the at least one dataunit. Means are provided for processing, at the plurality of storagenodes, the plurality of symbols using a map-function to generate aplurality of intermediate contexts. Means are provided for determining,responsive to receiving the plurality of intermediate contexts from theplurality of storage nodes, a reduce-function result using the pluralityof intermediate contexts.

Implementations may include one or more of the following features. Thesystem may further include: means for aggregating complete targetsubunits from incomplete subunit portions, where the plurality ofintermediate contexts includes incomplete subunit portions from themeans for processing the plurality of symbols; means for processing thecomplete target subunits using the map-function to generate additionalintermediate contexts; and means for adding the additional intermediatecontexts to the plurality of intermediate contexts.

The various embodiments advantageously apply the teachings ofdistributed storage networks and/or systems to improve the functionalityof such computer systems. The various embodiments include operations toovercome or at least reduce the issues in the previous storage networksand/or systems discussed above and, accordingly, are more reliableand/or efficient than other computing networks. That is, the variousembodiments disclosed herein include hardware and/or software withfunctionality to improve the efficient processing of data functions beexecuting those functions closer to the stored data. Accordingly, theembodiments disclosed herein provide various improvements to storagenetworks and/or storage systems.

It should be understood that language used in the present disclosure hasbeen principally selected for readability and instructional purposes,and not to limit the scope of the subject matter disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an example of a distributed storagesystem.

FIG. 2 schematically illustrates an example client architecture in whichthe distributed storage system of FIG. 1 may operate.

FIG. 3 schematically illustrates an example of a storage node of thedistributed storage system of FIG. 1.

FIG. 4 schematically illustrates an example of a controller node oraccess node of the distributed storage system of FIG. 1.

FIG. 5 schematically illustrates some example elements of a storagesystem for the distributed storage system of FIG. 1.

FIG. 6 schematically illustrates an example data unit being processed bya plurality of storage nodes.

FIG. 7 schematically illustrates an example data unit being processed bya plurality of storage nodes with the assistance of a storagecontroller.

FIG. 8 schematically illustrates an example configuration for encoding adata unit across symbols.

FIG. 9 schematically illustrates another example configuration forencoding a data unit across symbols.

FIG. 10 schematically illustrates another example configuration forencoding a data unit across symbols.

FIG. 11 schematically illustrates another example configuration forencoding a data unit across symbols.

FIG. 12 illustrates an example method of function processing acrossstorage nodes.

FIG. 13 illustrates an example method of function processing byaggregating incomplete subunits.

FIG. 14 illustrates an example method of coordinating functionprocessing across storage nodes for a target data set.

FIG. 15 illustrates an example method of function processing with symbolrecovery.

FIG. 16 illustrates an example method of symbol encoding to supportfunction processing.

FIG. 17 illustrates an example method of function processing usingserial operations between storage nodes.

FIG. 18 schematically illustrates some example elements for interfacingwith storage nodes of the distributed storage system of FIG. 1.

FIG. 19 illustrates an example method of configuring a data functionrequest.

FIG. 20 illustrates an example method of initiating encoding andstorage.

FIG. 21 illustrates an example method of initiating decoding andfunction processing.

DETAILED DESCRIPTION

FIG. 1 shows an embodiment of an example distributed storage system 1.In some embodiments, the distributed storage system 1 may be implementedas a distributed object storage system which is coupled to one or moreclients 10.1-10.n for accessing data objects through one or morecontroller or access nodes 20.1-10.n. The connection between thedistributed storage system 1 and clients 10 could, for example, beimplemented as a suitable data communication network. Clients 10 mayhost or interface with one or more applications that use data stored indistributed storage system 1. Such an application could, for example, bea dedicated software application running on a client computing device,such as a personal computer, a laptop, a wireless telephone, a personaldigital assistant or any other type of communication device that is ableto interface directly with the distributed storage system 1. However,according to alternative embodiments, the applications could, forexample, comprise a suitable file system which enables a general purposesoftware application to interface with the distributed storage system 1,an application programming interface (API) library for the distributedstorage system 1, etc. In some embodiments, access nodes 20 may includea file interface system for receiving file data requests from clients 10according to a file system protocol and access data in storage nodes30.1-30.40 using a different storage protocol, such as an object storageprotocol.

As further shown in FIG. 1, the distributed storage system 1 comprises aplurality of controller or access nodes 20 and a plurality of storagenodes 30 which may be coupled in a suitable way for transferring data,for example by means of a conventional data communication network suchas a local area network (LAN), a wide area network (WAN), a telephonenetwork, such as the public switched telephone network (PSTN), anintranet, the internet, or any other suitable communication network orcombination of communication networks. Access nodes 20, storage nodes 30and the computing devices comprising clients 10 may connect to the datacommunication network by means of suitable wired, wireless, optical,etc. network connections or any suitable combination of such networkconnections. Although the embodiment of FIG. 1 shows only three accessnodes 20 and forty storage nodes 30, according to alternativeembodiments the distributed storage system 1 could comprise any othersuitable number of storage nodes 30 and, for example, two, three or moreaccess nodes 20 coupled to these storage nodes 30.

These access nodes 20 and storage nodes 30 may be built asgeneral-purpose computers. Alternatively, they may be physically adaptedfor arrangement in large data centers, where they are arranged inmodular racks 40.1-40.n comprising standard dimensions. Exemplary accessnodes 20 and storage nodes 30 may be dimensioned to take up a singleunit of such racks 40, which is generally referred to as 1U. Such anexemplary storage node may use a low-power processor and may be equippedwith ten or twelve high capacity serial advanced technology attachment(SATA) disk drives and is connectable to the network over redundantEthernet network interfaces. An exemplary access node 20 may comprisehigh-performance servers and provide network access to clients 10 overmultiple high bandwidth Ethernet network interfaces. Data can betransferred between clients 10 and such access nodes 20 by means of avariety of network protocols including hypertext transfer protocol(HTTP)/representational state transfer (REST) object interfaces,language-specific interfaces such as Microsoft .Net, Python or C, etc.Additionally, such access nodes may comprise additional high bandwidthEthernet ports to interface with the storage nodes 30. In someembodiments, HTTP/REST protocols complying with the Amazon SimpleStorage Service (S3) object storage service may enable data transferthrough a REST application protocol interfaces (API). Such access nodes20 may operate as a highly available cluster of controller nodes withone or more integrated and/or independent interface systems, and providefor example shared access to the storage nodes 30, metadata caching,protection of metadata, etc.

As shown in FIG. 1 several storage nodes 30 can be grouped together, forexample because they are housed in a single rack 40. For example,storage nodes 30.1-30.4 and 30.37-30.40 each are respectively groupedinto racks 40.1 and 40.n. Access nodes 20 may be located in the same ordifferent racks as the storage nodes to which the access nodes connect.A rack may have multiple access nodes, for example rack 40.1, a singleaccess node as rack 40.n, or no access nodes (not shown) and rely on anaccess node in another rack or storage nodes or clients with built-inaccess node and/or controller node capabilities. These racks are notrequired to be located at the same location, they are oftengeographically dispersed across different data centers, such as forexample rack 40.1-40.3 can be located at a data center in Europe,40.4-40.7 at a data center in the USA and 40.8-40.10 at a data center inChina.

FIG. 2 is a block diagram of an example storage network 50 using aclient architecture. In some embodiments, distributed storage system 1may be embodied in such a storage network 50. As shown, storage network50 can include multiple client devices 60 capable of being coupled toand in communication with a storage network 50 via a wired and/orwireless network 70 (e.g., public and/or private computer networks inany number and/or configuration (e.g., the Internet, an intranet, acloud network, etc.)), among other examples that may include one clientdevice 60.1 or two or more client devices 60 (e.g., is not limited tothree client devices 60.1-60.3).

A client device 60 can be any computing hardware and/or software (e.g.,a thick client, a thin client, or hybrid thereof) capable of accessingstorage system 80 utilizing network 70. Each client device 60, as partof its respective operation, relies on sending input/output (I/O)requests to storage system 80 to write data, read data, and/or modifydata. Specifically, each client device 60 can transmit I/O requests toread, write, store, communicate, propagate, and/or transportinstructions, data, computer programs, software, code, routines, etc.,to storage system 80. Client device(s) 60 and storage system 80 maycomprise at least a portion of a client-server model. In general,storage system 80 can be accessed by client device(s) 60 and/orcommunication with storage system 80 can be initiated by clientdevice(s) 60 through a network socket (not shown) utilizing one or moreinter-process networking techniques. In some embodiments, client devices60 may access one or more applications to use or manage a distributedstorage system, such as distributed storage system 1 in FIG. 1.

FIG. 3 shows a schematic representation of an embodiment of one of thestorage nodes 30. Storage node 30.1 may comprise a bus 310, a processor320, a local memory 330, one or more optional input units 340, one ormore optional output units 350, a communication interface 360, a storageelement interface 370, and two or more storage elements 300.1-300.10.Bus 310 may include one or more conductors that permit communicationamong the components of storage node 30.1. Processor 320 may include anytype of conventional processor or microprocessor that interprets andexecutes instructions. Local memory 330 may include a random accessmemory (RAM) or another type of dynamic storage device that storesinformation and instructions for execution by processor 320 and/or aread only memory (ROM) or another type of static storage device thatstores static information and instructions for use by processor 320.Input unit 340 may include one or more conventional mechanisms thatpermit an operator to input information to the storage node 30.1, suchas a keyboard, a mouse, a pen, voice recognition and/or biometricmechanisms, etc. Output unit 350 may include one or more conventionalmechanisms that output information to the operator, such as a display, aprinter, a speaker, etc. Communication interface 360 may include anytransceiver-like mechanism that enables storage node 30.1 to communicatewith other devices and/or systems, for example mechanisms forcommunicating with other storage nodes 30 or access nodes 20 such as forexample two 1 gigabit (Gb) Ethernet interfaces.

Storage element interface 370 may comprise a storage interface such asfor example a SATA interface or a small computer system interface (SCSI)for connecting bus 310 to one or more storage elements 300, such as oneor more local disks, for example 3 terabyte (TB) SATA disk drives, andcontrol the reading and writing of data to/from these storage elements300. In one exemplary embodiment as shown in FIG. 2, such a storage node30.1 could comprise ten or twelve 3 TB SATA disk drives as storageelements 300.1-300.10 and in this way storage node 30.1 would provide astorage capacity of 30 TB or 36 TB to the distributed storage system 1.According to the exemplary embodiment of FIG. 1 and in the event thatstorage nodes 30.2-30.40 are identical to storage node 30.1 and eachcomprise a storage capacity of 36 TB, the distributed storage system 1would then have a total storage capacity of 1440 TB.

As is clear from FIGS. 1 and 3 the distributed storage system 1comprises a plurality of storage elements 300. As will be described infurther detail below, the storage elements 300, could also be referredto as redundant storage elements 300 as the data is stored on thesestorage elements 300 such that none or a specific portion of theindividual storage elements 300 on its own is critical for thefunctioning of the distributed storage system. Each of the storage nodes30 may comprise a share of these storage elements 300.

As shown in FIG. 1 storage node 30.1 comprises ten storage elements300.1-300.10. Other storage nodes 30 could comprise a similar amount ofstorage elements 300, but this is, however, not essential. Storage node30.2 could, for example, comprise six storage elements 300.11-300.16,and storage node 30.3 could, for example, comprise four storage elements300.17-300.20. As will be explained in further detail below, thedistributed storage system 1 may be operable as a distributed objectstorage system to store and retrieve a data object comprising data (e.g.64 megabytes (MB) of binary data) and a data object identifier foraddressing this data object, for example, a universally uniqueidentifier such as a globally unique identifier (GUID). Embodiments ofthe distributed storage system 1 may operate as a distributed objectstorage system. Storing the data offered for storage by the applicationin the form of a data object, also referred to as object storage, mayhave specific advantages over other storage schemes such as block-basedstorage or file-based storage.

The storage elements 300 or a portion thereof may be redundant andoperate independently of one another. This means that if one particularstorage element 300 fails its function it can easily be taken on byanother storage element 300 in the distributed storage system 1.However, as will be explained in more detail further below, the storageelements 300 are capable of providing redundancy without having to workin synchronism, as is for example the case in many well-known redundantarray of independent disks (RAID) configurations, which sometimes evenrequire disc spindle rotation to be synchronised. Furthermore, theindependent and redundant operation of the storage elements 300 mayallow a suitable mix of types of storage elements 300 to be used in aparticular distributed storage system 1. It is possible to use forexample storage elements 300 with differing storage capacity, storageelements 300 of differing manufacturers, using different hardwaretechnology such as for example conventional hard disks and solid statestorage elements, using different storage interfaces such as for exampledifferent revisions of SATA, parallel advanced technology attachment(PATA), and so on. This may result in advantages relating to scalabilityand flexibility of the distributed storage system 1 as it allows foradding or removing storage elements 300 without imposing specificrequirements to their design in correlation to other storage elements300 already in use in the distributed object storage system.

FIG. 4 shows a schematic representation of an embodiment of thecontroller or access node 20. Access node 20 may include storagecontroller node functions and/or file system interface functions forclient systems using file system protocols to access data stored in dataobjects in storage nodes 30. Access node 20 may comprise a bus 210, aprocessor 220, a local memory 230, one or more optional input units 240,one or more optional output units 250. In some embodiments, access node20 may include object storage management functions, including objectstorage interface functions, version control management, and/orreplication engines.

Bus 210 may include one or more conductors that permit communicationamong the components of access node 20. Processor 220 may include anytype of conventional processor or microprocessor that interprets andexecutes instructions. Local memory 230 may include a random accessmemory (RAM) or another type of dynamic storage device that storesinformation and instructions for execution by processor 220 and/or aread only memory (ROM) or another type of static storage device thatstores static information and instructions for use by processor 320and/or any suitable storage element such as a hard disc or a solid statestorage element. An optional input unit 240 may include one or moreconventional mechanisms that permit an operator to input information tothe access node 20 such as a keyboard, a mouse, a pen, voice recognitionand/or biometric mechanisms, etc. Optional output unit 250 may includeone or more conventional mechanisms that output information to theoperator, such as a display, a printer, a speaker, etc. Communicationinterface 260 may include any transceiver-like mechanism that enablesaccess node 20 to communicate with other devices and/or systems, forexample mechanisms for communicating with other storage nodes 30 oraccess nodes 20 such as for example two 10 Gb Ethernet interfaces.

According to an alternative embodiment, the access node 20 could have anidentical design as a storage node 30, or according to still a furtheralternative embodiment one of the storage nodes 30 of the distributedobject storage system could perform both the function of an access node20 and a storage node 30. According to still further embodiments, thecomponents of the access node 20 as described in more detail below couldbe distributed amongst a plurality of access nodes 20 and/or storagenodes 30 in any suitable way. According to still a further embodiment,the clients 10 may run an access node 20. According to still furtherembodiments, access node 20 may be embodied in separate controller nodesand interface nodes with or without redundancy among the controllernodes and/or interface nodes.

FIG. 5 schematically shows selected modules of a storage node,controller node, or combination thereof. Storage system 500 may beconfigured as a node with an architecture and/or hardware similar tocontroller nodes and/or storage nodes. Storage system 500 mayincorporate elements and configurations similar to those shown in FIGS.1-4. For example, storage system 500 may include a plurality of storagenodes 30 configured with the modules shown. In some embodiments, inaddition to the modules hosted by storage nodes 30, one or more modulesof storage system 500, such as storage interface 520, functioncoordinator 550, incomplete subunit processor 560, and/or symbolrecovery engine 570 may be hosted by a controller or access node 20configured as a storage controller.

Storage system 500 may include a bus 510 interconnecting at least onecommunication unit 512, at least one processor 514, and at least onememory 516. Bus 510 may include one or more conductors that permitcommunication among the components of access system 500. Communicationunit 512 may include any transceiver-like mechanism that enables accesssystem 500 to communicate with other devices and/or systems. Forexample, communication unit 512 may include wired or wireless mechanismsfor communicating with file system clients, other access systems, and/orone or more object storage systems or components, such as storage nodesor controller nodes. Processor 514 may include any type of processor ormicroprocessor that interprets and executes instructions. Memory 516 mayinclude a random access memory (RAM) or another type of dynamic storagedevice that stores information and instructions for execution byprocessor 514 and/or a read only memory (ROM) or another type of staticstorage device that stores static information and instructions for useby processor 514 and/or any suitable storage element such as a hard discor a solid state storage element.

Storage system 500 may include or have access to one or more databasesand/or specialized data stores, such metadata store 580 and data store590. Databases may include one or more data structures for storing,retrieving, indexing, searching, filtering, etc. of structured and/orunstructured data elements. In some embodiments, metadata store 580 maybe structured as reference data entries and/or data fields indexed bymetadata key value entries related to data objects stores in data store590. Data store 590 may include data objects comprised of object data(such as host data), some amount of metadata (stored as metadata tags),and a GUID. Metadata store 580, data store 590, and/or other databasesor data structures may be maintained and managed in separate computingsystems, such as storage nodes, controller nodes, or access nodes, withseparate communication, processor, memory, and other computing resourcesand accessed by storage system 500 through data access protocols.Metadata store 580 and data store 590 may be shared across multiplestorage systems 500.

Storage system 500 may include a plurality of modules or subsystems thatare stored and/or instantiated in memory 516 for execution by processor514. For example, memory 516 may include a storage interface 520configured to receive, process, and respond to data requests and/or dataoperation or function commands from client systems or other nodes indistributed storage system 1. Memory 516 may include anencoding/decoding engine 528 for encoding and decoding symbolscorresponding to data units (files, objects, messages, etc.) stored indata store 590. Memory 516 may include a function processor 536 forprocessing data operations or functions received from a client or hostsystem, such as performing a map-reduce function on a data set stored indata store 590. Memory 516 may include a function coordinator 550 forcoordinating data function or operation processing among a plurality ofstorage nodes. Memory 516 may include an incomplete subunit processor560 for aggregating complete subunits from partial subunits storedacross symbols in some embodiments. Memory 516 may include a symbolrecovery engine 570 for recovering symbols that are the result of afailed decode operation by encoding/decoding engine 528. In someembodiments, encoding/decoding engine 528, function processor 536,function coordinator 550, incomplete subunit processor 560, and/orsymbol recovery engine 570 may be integrated into storage interface 520and/or managed as separate libraries or background processes (e.g.daemon) through an API or other interface.

Storage interface 520 may include an interface protocol or set offunctions and parameters for storing, reading, and otherwise managingdata requests to data store 590. For example, storage interface 520 mayinclude functions for reading, writing, modifying, or otherwisemanipulating data objects and/or files, as well as their respectiveclient or host data and metadata in accordance with the protocols of anobject or file storage system. In some embodiments, storage interface520 may further enable execution of data operations for data store 590and/or metadata store 580. For example, storage interface 520 mayinclude protocols and/or interfaces for receiving data function requeststhat may include defining functions, target data sets, and/or resultformatting and delivery, as well as executing those functions againstdata store 590.

In some embodiments, storage interface 520 may include a plurality ofhardware and/or software modules configured to use processor 514 andmemory 516 to handle or manage defined operations of storage interface520. For example, storage interface 520 may include a client requesthandler 522, a metadata manager 524, and a storage manager 526. For anygiven client request, storage interface 520 may receive a client requestthrough client request handler 522 and determine one or more operationsbased on the content of the request. These operations may includemetadata operations handled by metadata manager 524 and/or object dataoperations handled by storage manager 526, including encoding anddecoding operations. In some embodiments, data processing operations maybe handled by storage interface 520 by calling one or more othermodules, such as function processor 536 and/or function coordinator 550.The results of these operations may be processed, formatted, andreturned by client request handler 522.

Client request handler 522 may include an interface and/or communicationevent-based condition for receiving data requests and/or operationalcommands from one or more clients. For example, client systems may sendan object data request over a network connection and addressed tostorage system 500 or a port or component thereof. Client requesthandler 522 may receive these requests and parse them according to theappropriate communication and storage protocols. For example, clientrequest handler 522 may identify a transaction identifier, a clientidentifier, an object identifier (object name or GUID), a dataoperation, and additional parameters for the data operation, if any,from the received message or messages that make up the object datarequest. Similarly, operational commands may include syntax andparameters for accessing data stored according to a specific filesystem. Operational commands may also relate to the execution of datafunctions by storage system 500.

In some embodiments, client request handler 522 may be configured formanaging data operations to be executed by storage system 500. Forexample, a client system may be able to define one or more datafunctions to be executed against a data set stored in data store 590without transferring the data set to the client system. In someembodiments, data stored in erasure encoded symbols in data store 590may be processed through at least one function (in a set of functions)by the storage node storing the symbol and an intermediate context maybe returned for further processing, such as by another storage nodeusing the same function or another function in the set.

Client request handler 522 may include one or more operations formanaging data operation requests from a client system. For example, uponreceiving a request or command that relates to a data operation, clientrequest handler 522 may identify the management operation and/or parsethe components of a complete data function operation. In someembodiments, a complete data operation request may include requestparameters for a function definition, a target data set, and a resultformat. Client request handler 522 may include a function definitionmodule 522.1, a data set selector 522.2, and/or a result formatter 522.3for identifying, determining, or otherwise parsing the parameters of thedata operation request.

In some embodiments, function definition module 522.1 may include aninterface, function, or logic to receive and/or determine the set offunctions to be used in a data function operation. For example, the setof functions may include a function or set of parameters that may beapplied to a subunit identification function for identifying datasubunits. Example subunit identification functions might include logicfor identifying sentences within a block of text, a frame of data withina video image file, or a shape within a graphics file. In someembodiments, a subunit identification function may include a set ofsubunit parameters that define the portions of a data unit that shouldbe treated as a subunit for the purposes of the set of functions. Theset of functions may include a map-function, which may provide logic foroperating on a subunit to determine an intermediate context for thatsubunit. For example, the map-function may count the nouns in asentence, the faces in a frame of video, or the vertices in a shape andreturn a numeric value or type-value pair for each parameter of thesubunit being determined by the map-function. A map-function may be aparallel-function that allows each subunit to be processed independentlyor a serial-function where each intermediate context provides one ormore values for use in applying the serial-function to the next subunit.The set of functions may include a reduce-function, which provides logicfor providing an aggregate or result value for the intermediate contextsdetermined for each subunit. The set of functions may also includeterminal conditions, such as values or parameters to seed anotherfunction (e.g., a map or reduce function) or conditions signalling afinal subunit and a result-function. In some embodiments, functiondefinition module 522.1 may include an API or user interface forreceiving selections of function types and parameters and may be sentfrom a client system.

In some embodiments, data set selector 522.2 may include an interface,function, or logic to receive and/or determine target data set to beprocessed using the set of functions for a particular data operation.For example, data set selector 522.2 may define the bounds of a set ofdata using any physical or logical grouping appropriate to theparticular set of functions. Data set selector 522.2 may be configuredfor the type of data stored in data store 590 and/or the metadata frommetadata store 580 that may be used to index the data. For example, dataset selector 522.2 may be able to target a data object, set of dataobjects defined by some selection criteria, a bucket or other logicalvolume, or a similar set of parameters for defining data of interest. Asanother example, data set selector 522.2 may be able to target a datafile, a set of data files defined by some selection criteria, an inodeor other logical volume, or a similar set of parameters for definingdata of interest. As still another example, data selector 522.2 may beable to target a physical storage location using a starting address andending address, starting address and length, or similar boundaryconditions that map to physical addresses or their contents. In someembodiments, data set selector 522.2 may define a total data setcomprised of a plurality of data units, such as files, objects, ormessages within the total data set. The plurality of data units may eachbe comprised of a plurality of subunits that may be the target ofdefined functions, such as map-functions. In some embodiments, data setselector 522.2 may include an API or user interface for receivingselections of data set parameters or identifiers that may be sent from aclient system.

In some embodiments, result formatter 522.3 may include an interface,function, or logic to receive and/or determine the format of the resultsto be returned to a requesting system, such as a client or host system.For example, result formatter 522.3 may receive the result output fromapplying the set of functions to the target data set and format inaccordance with the preferences of the requesting system, such assimplifying results to a fixed value, delta value, array of values,file, object, metadata table, etc. In some embodiments, a map-reducefunction set may return a final reduce-result in a defined format. Forexample, the map-reduce function set may return a total number of words,sentences, and paragraphs in a large text file or text object for novelby formatting three numeric values preceded by appropriate tags inaccordance with a defined syntax, such as comma separated values. Aresult may be returned for each of the plurality of data units and/orfor the total data set. In some embodiments, result formatter 522.3 mayinclude an API or user interface for returning result values to a clientsystem.

Metadata manager 524 may include interfaces, functions, and/orparameters for creating, modifying, deleting, accessing, and/orotherwise managing object or file metadata, such as metadata stored inmetadata store 580. For example, when a new object is written to datastore 590, at least one new metadata entry may be created in metadatastore 580 to represent parameters describing or related to the newlycreated object. Metadata manager 524 may generate and maintain metadatathat enables metadata manager 524 to locate object or file metadatawithin metadata store 580. For example, metadata store 580 may beorganized as a key-value store and object metadata may include keyvalues for data objects and/or operations related to those objects thatare indexed with a key value that include the object identifier or GUIDfor each object. In some embodiments, metadata manager 524 may alsomanage metadata stored in data store 590 with the data objects or files,such as metadata tags or headers. Metadata manager 524 may work inconjunction with storage manager 526 to create, modify, delete, accessor otherwise manage metadata stored as tags or headers within data store590.

Storage manager 526 may include interfaces, functions, and/or parametersfor reading, writing, and deleting data elements in data store 590. Forexample, object PUT commands may be configured to write objectidentifiers, object data, and/or object tags to an object store. ObjectGET commands may be configured to read data from an object store. ObjectDELETE commands may be configured to delete data from object store, orat least mark a data object for deletion until a future garbagecollection or similar operation actually deletes the data or reallocatesthe physical storage location to another purpose.

In some embodiments, storage manager 526 may oversee writing and readingdata elements that are erasure encoded on the storage medium on whichdata store 590 is stored. When a message or data unit, such as a file ordata object, is received for storage, storage manager 526 may pass thefile or data object through an erasure encoding engine, such asencoding/decoding engine 528. The data unit may be divided into symbolsand the symbols encoded into erasure encoded symbols 592 for storage indata store 590. In some embodiments, the symbols may be distributedamong a plurality of storage nodes to assist with fault tolerance,efficiency, recovery, and other considerations.

When the data unit is to be accessed or read, storage manager 526 mayidentify the storage locations for each symbol, such as using a dataunit/symbol map 582 stored in metadata store 580. Erasure encodedsymbols 592 may be passed through an erasure decoding engine, such asencoding/decoding engine 528 to return the original symbols that made upthe data unit to storage manager 526. The data unit can then bereassembled and used by storage manager 526 and other subsystems ofstorage interface 520 to complete the data access operation. Storagemanager 526 may work in conjunction with metadata manager 524 formanaging metadata, such as storage locations, versioning information,operation logs, etc. Storage manager 526 may work with encoding/decodingengine 528 for storing and retrieving erasure encoded symbols 592 indata store 590. Storage manager 526 may work in conjunction withfunction processor 536, function coordinator 550, incomplete subunitprocessor 560, and symbol recovery engine 570 to manage symbols and dataunits for the function processing.

In some embodiments, storage interface 520 may support metadata store580 being distributed across multiple systems, such as a plurality ofaccess systems. Metadata store 580 and/or portions thereof may besharded data stores, wherein the data stores are partitioned intosegments stored in different computing systems. Storage interface 520may include the functions for locating and accessing relevant portionsof the sharded data base.

Encoding/decoding engine 528 may include a set of functions andparameters for storing, reading, and otherwise managing encoded data,such as erasure encoded symbols 592, in data store 590. For example,encoding/decoding engine 528 may include functions for encoding a userdata symbol into an erasure encoded data symbol and decoding an erasureencoded data symbol back into the original user data symbol. In someembodiments, encoding/decoding engine 528 may be included in the writepath and/or read path for data store 590 that is managed by storagemanager 526. In some embodiments, the encoding and decoding functionsmay be placed in separate encoding engines and decoding engines withredundant and/or shared functions where similar functions are used byboth encoding and decoding operations.

In some embodiments, encoding/decoding engine 528 may include aplurality of hardware and/or software modules configured to useprocessor 514 and memory 516 to handle or manage defined operations ofencoding/decoding engine 528. For example, encoding/decoding engine 528may include an erasure coding configuration 530, a symbol partitioner532, and encoders/decoders 534.

Erasure coding configuration 530 may include functions, parameters,and/or logic for determining the operations used to partition data unitsinto symbols, encode, and decode those symbols. For example, variouserasure coding algorithms exist for providing forward error correctionbased on transforming a message of a certain number of symbols into alonger message of more symbols such that the original message can berecovered from a subset of the encoded symbols. In some embodiments, amessage may be split into a fixed number of symbols and these symbolsare used as input for erasure coding. A systematic erasure codingalgorithm may yield the original symbols and a fixed number ofadditional parity symbols. The sum of these symbols may then be storedto one or more storage locations.

In some embodiments, erasure coding configuration 530 may enableencoding/decoding engine 528 to be configured from available codingalgorithms 530.1 and encoded block sizes 530.2 supported by storagesystem 500. For example, coding algorithms 530.1 may enable selection ofan algorithm type, such as parity-based, low-density parity-check codes,Reed-Solomon codes, etc., and one or more algorithm parameters, such asnumber of original symbols, number of encoded symbols, code rate,reception efficiency, etc. Encoded block size 530.2 may enable selectionof a block size for encoded symbols. For example, the encoded block sizemay be selected to align with storage media considerations, such as anerase block size for sold state drives (SSDs), and/or a symbol size thataligns with data unit and/or subunit parameters for data operations. Insome embodiments, erasure coding configuration 530 may include aninterface or API, such as a configuration utility, to enable a clientsystem to select one or more parameters of coding algorithm 530.1 and/orencoded block size 530.2. For example, a user may configure codingalgorithm 530.1 and encoded block size 530.2 to correspond to parametersof the target data set, such as aligning message size with functionaldata units and/or symbol size and number with subunits used in datafunction processing.

Symbol partitioner 532 may include functions, parameters, and/or logicfor receiving a message for encoding and partitioning the message into aseries of original symbols based on the data in the message. Forexample, a default symbol partitioning operation may receive a messageand use a symbol size defined by erasure coding configuration 530 topartition the message into fixed number of symbols for encoding. In someembodiments, symbol partitioner 532 may include configurable symbolparameters 532.1 that may be selected to improve subsequent datafunction processing. For example, symbol parameters 532.1 may define asymbol size that aligns with subunit length in a data unit with fixedsize subunits or a maximum subunit length. Symbol parameters 532.1 mayinclude one or more parameters stored in a configuration table or otherdata structure for reference by symbol partitioner 532.

In some embodiments, symbol partitioner 532 may support the use of anoverlap data portion 532.2 for shingling symbols such that each symbolincludes a repeated portion of an adjacent symbol. For example, overlapdata portion 532.2 may include an overlap data length parameter thatindicates a portion of data equal to the length parameter that will berepeated in the next symbol and appends it to the end of current symbol.Repeated data portions in overlap data portion 532.2 may enable moreefficient identification of subunits for data function processing at thestorage node and reduce the frequency of incomplete subunits in somesymbol configurations. In some embodiments, overlap data portion 532.2may be determined by a user to align with one or more features of thetarget data set or data functions. For example, overlap data portion532.2 may be set to a length parameter equal to a maximum subunit sizeor length to assure that no subunits are only available split acrosssymbols (and would need to be aggregated from incomplete subunitportions in two symbols to be processed). In some embodiments, knowledgeof predefined data formats, such as specific file or object types (e.g.Moving Pictures Expert Group (MPEG), comma separated values (CSV),graphics file types, etc.), may enable overlap data portion 532.2 to beset to a selected value that maximizes a probability that completeversions of all subunits will be available in at least one symbol and/ormanages the trade-off between redundant subunits and exception handlingfor incomplete subunits. In some embodiments, symbol partitioner 532 maybe configured to receive predetermined overlap length parameters fromthe client system for use in determining overlap data portion 532.2.Subunits that appear in multiple symbols may be skipped or otherwisehandled during intermediate context forwarding and/or functioncoordination.

In some embodiments, symbol partitioner 532 may be configured to alignsymbols with subunits using a subunit identifier 532.3. For example,subunit identifier 532.3 may include logic for applying asubunit-function, such as subunit-function 540.1, to a data unit ormessage to determine the boundaries of each subunit. Subunit identifier532.3 may be configured with subunit parameters corresponding toterminal markers or conditions for each subunit, such as a startindicator and an end indicator. In some data sets, subunit startindicators and end indicators may include flags, tags, codes, or otherindicators to denote subunit boundaries and enable serial detection ofsubunits, while others may include a set of criteria applied to the dataunit as a whole to determine the subunit boundaries. In someembodiments, subunit identifier 532.3 may return an identified subunit,list of subunits, subunit boundaries, subunit lengths, or other subunitparameters that enable subunits to be selected from the data unit foralignment with symbols. For example, in some embodiments, identifiedsubunits may be assigned to each symbol by symbol partitioner 532 on aone-to-one basis such that complete subunits are available in eachsymbol.

In some embodiments, symbol partitioner 532 may be configured to usesymbol padding 532.4 to assist in aligning subunits with symbols. Forexample, erasure coding configuration 530 may support a fixed encodedblock size and/or encoded block sizes on defined increments, such as amultiple of a storage block, page, line, or other physical storage unit,in encoded block size 530.2. Symbol partitioner 532 may use symbolidentifier 532.3 to identify a next subunit, determine the subunitlength, and determine additional bits needed to meet encoded block size530.2. Symbol padding 532.4 may fill in the difference withpredetermined data values or data patterns, such as inserting nullvalues into the remaining logical positions between the end of thesubunit data and the end of the symbol.

Encoder/decoder 534 may include hardware and/or software encoders anddecoders for implementing coding algorithm 530.1. For example,encoding/decoding engine 528 may include a plurality of register-basedencoders and decoders for calculating parity for a symbol and returningerasure encoded symbols 592. In some embodiments, encoder/decoder 534may be integrated in the write path and read path respectively such thatdata to be written to storage media and read from storage media passthrough encoder/decoder 534 for encoding and decoding in accordance withcoding algorithm 530.1.

Function processor 536 may include a set of functions and parameters foridentifying target erasure encoded symbols, such as erasure encodedsymbols 592, in data store 590, and processing them through one or moredata functions, such as user data processing functions received throughclient request handler 522. For example, function processor 536 may beimplemented in each storage node of storage system 500 to enable localprocessing of local erasure encoded symbols 592 in the same storage nodeas function processor 536 (using local memory 516, local processor 514,and local storage media for data store 590). In some embodiments,function processor 536 may operate in conjunction with the decodingfunctions of encoding/decoding engine 528 to retrieve and decode erasureencoded symbols 592 such that function processor 536 may operate on theoriginal or decoded symbols.

User data processing functions may include data functions that operateon stored user data regardless of physical storage location, encoding,redundancy, encryption, and storage management functions used by thestorage system to manage data storage. For example, user data processingfunctions may include data transformations, extractions, abstractions,feature identification, and other processes to support clientapplication processing of data for applications like data analysis,artificial intelligence training, image processing, pattern recognition,etc.

In some embodiments, function processor 536 may include a plurality ofhardware and/or software modules configured to use processor 514 andmemory 516 to handle or manage defined operations of function processor536. For example, function processor 536 may include a target symbolselector 538, a subunit identifier 540, a prior intermediate contexthandler 542, processing functions 544, and a context generator 546.

Target symbol selector 538 may include functions, parameters, and/orlogic for selecting a target symbol for executing one or more functionsagainst. For example, target symbol selector 538 may identify a targetsymbol among erasure encoded symbols 592 for retrieval and decodingthrough a local instantiation of encoding/decoding engine 528. Thus, alocal encoding/decoding engine of a particular storage node may targetlocal symbols stored in local storage media for data store 590. In someembodiments, function processor 536 may receive a function requestmessage that indicates a target symbol and target symbol selector 538may parse the symbol identification information from the functionrequest message to determine the target symbol. For example, functioncoordinator 550 may send a function request to a target storage nodestoring the target symbol or another storage node may send the functionrequest along with an intermediate context where the storage node hasidentified the next symbol and the storage node storing that symbol.Target symbol selector 538 may return a target symbol that has beendecoded into the original symbol for further processing by functionprocessor 536.

Subunit identifier 540 may include functions, parameters, and/or logicfor identifying one or more subunits and/or subunit portions from atarget symbol, such as the target symbol returned by target symbolselector 538. For example, subunit identifier 540 may include logic forapplying a subunit-function, such as subunit-function 540.1, to a dataunit or message to determine the boundaries of each subunit. Subunitidentifier 540 may be configured with subunit parameters correspondingto terminal markers or conditions for each subunit, such as a startindicator and an end indicator. In some data sets, subunit startindicators and end indicators may include flags, tags, codes, or otherindicators to denote subunit boundaries and enable serial detection ofsubunits, while others may include a set of criteria applied to the dataunit as a whole to determine the subunit boundaries. Subunit-function540.1 may include the logic, parameters, or conditions to be evaluatedfor identifying subunits from the symbol. For example, sub-unit function540.1 may be provided as part of function definition 522.1 in clientrequest handler 522 for the purpose of identifying subunit boundarieswithin data units. In some embodiments, subunit identifier 540 mayreturn an identified subunit, list of subunits, subunit boundaries,subunit lengths, or other subunit parameters that enable subunits to beselected from the symbol for further processing by function processor536. In some embodiments, subunit identifier 540 may return one or morecomplete subunits and one or more incomplete subunit portions wheresubunit boundaries did not align with symbol boundaries.

Prior intermediate context 542 may include functions, parameters, and/orlogic for identifying one or more prior intermediate contexts to be usedin processing data functions and/or generating the next intermediatecontext. For example, prior intermediate context 542 may identify aprior intermediate context that may be used as an input to processingfunctions 544. In some embodiments, function processor 536 may receive afunction request message that includes an intermediate context and priorintermediate context 542 may parse the intermediate context from thefunction request message to determine the prior intermediate context forfurther processing by function processor 536. For example, functioncoordinator 550 may send an intermediate context to a target storagenode storing the next target symbol or another storage node may send theintermediate context where the storage node has identified the nextsymbol and the storage node storing that symbol. The contents ofintermediate contexts may be further discussed below with regard tocontext generator 546. Prior intermediate context 542 may return atleast one intermediate context for use in further processing by functionprocessor 536. In some embodiments, prior intermediate context 542 mayreceive a prior intermediate context when processing the first subunitof a data unit that may be based on a seed context used for new dataunits (which may be included in function definition 522.1 and/orreceived as part of a function processing request), a final result froma prior data unit, and/or a final intermediate context from a prior dataunit.

Processing functions 544 may include functions, parameters, and/or logicfor processing data subunits to generate an intermediate and/or finalresult. For example, processing functions 544 may include user selectedand/or defined functions for processing application data to returndesired results. In some embodiments, processing functions 536 mayinclude subunit processing functions configured to process or transformthe data contents of a target subunit into an intermediate result storedor communicated in an intermediate context. Processing functions 544 mayreturn one or more result values for further processing by functionprocessor 536.

For example, processing functions 544 may include one or moreserial-functions 544.1 that receive prior intermediate context data(identified by prior intermediate context 542), apply serial-function544.1 to the target symbol (sometimes using some or all of the priorintermediate context data), and generate a new intermediate context forforwarding to the next storage node containing the next symbol.Serial-function 544.1 may include a function that sequentially processessubunits to generate a serial-function result to be returned to theclient system, with or without further processing of the finalserial-function result. In some embodiments, the intermediate result ofeach subunit is dependent on the intermediate result of at least theimmediately prior subunit and/or all prior subunits in a processingorder for serial-function 544.2, such as the subunit order in theoriginal data unit before partitioning into symbols.

As another example, processing functions 544 may include one or moremap-functions 544.2 that process the target data subunit and return anintermediate context for aggregation or further processing. Mappingfunctions 544.2 may include functions that map a target subunit to anintermediate context used for further processing. In some embodiments,mapping functions 544.2 may include a function that processes thesubunit regardless of order or is otherwise configured to enableparallel processing of subunits. In some embodiments, mapping functions544.2 may receive and use a prior intermediate context (identified byprior intermediate context 542) to calculate the new intermediatecontext. Prior intermediate contexts for mapping functions 544.2 may notbe dependent on processing order or an immediately prior subunit, suchas using an intermediate context from a prior data unit or another seedvalue that is not the intermediate context of the immediately priorsubunit.

Context generator 546 may include functions, parameters, and/or logicfor generating an intermediate context from the results of processingfunctions 544. For example, results of processing functions 544 may beformatted and packaged with other context data for passing to a nextstorage node, function coordinator 550, or another module for continuedprocessing. In some embodiments, context generator 546 may be configuredto handle function result data 546.1 and incomplete subunit data 546.2and format an intermediate context for delivery to an intermediatecontext destination 546.3. For example, function result data 546.1 maybe the result data of processing functions 544. Incomplete subunit data546.2 may include any incomplete subunit portions in a symbol that werenot processed through processing functions 544, such as incompletesubunit portions identified by subunit identifier 540.

Intermediate context destination 546.3 may include logic for identifyingthe destination for the intermediate context. For example, a symbolprocessing request may specify the destination for the intermediatecontext, such as a next storage node or function coordinator 550. Insome embodiments, intermediate context destination 546.3 may beidentified by using a symbol identifier, data unit identifier, or otherindex and accessing data unit/symbol map 582 in metadata store 580 todetermine a next symbol and/or next storage node storing the nextsymbol. In some embodiments, intermediate context destination 546.3 mayinclude exception handling for conditions that suggest special handling,such as the presence of incomplete subunits, failed symbol recovery,and/or terminal conditions triggering final result or other specialprocessing. For example, intermediate context destination 546.3 maydetermine the destination of an intermediate context including one ormore incomplete subunit portions to be incomplete subunit processor 560,with or without prior intermediate context and/or function results546.1. Intermediate context destination 546.3 may determine thedestination of an intermediate context aligning with terminal conditionsto function coordinator 550 or a terminal conditions handler (similar toterminal conditions handler 556) in function processor 536. Intermediatecontext destination 546.3 may determine a destination for the priorintermediate context to be symbol recovery engine 570 (or functioncoordinator 550) in response to encoding/decoding engine 528 failing todecode a symbol, such that the prior intermediate condition is availablefor further processing if and when the symbol is recovered.

Function coordinator 550 may include a set of functions and parametersfor coordinating symbol processing among a plurality of storage devices,where each storage device includes at least one locally stored encodedsymbol, such as erasure encoded symbols 592, in data store 590, andreturning a result through client request handler 522. For example,function coordinator 550 may enable a storage controller and/or astorage node configured for the function coordinator role to receive adata function request through client request handler 522, determine oneor more target data units in the target data set, identify the pluralityof storage nodes storing symbols for the target date units, and initiatea distributed function processing operation across the plurality ofstorage nodes.

In some embodiments, function coordinator 550 may act as a centralcontroller for distributed function processing, where functioncoordinator 550 sends function processing requests to each of theplurality of storage nodes with target symbols. For example, individualfunction processing requests for each symbol may be sent sequentially orin parallel and intermediate contexts from each request may be returnedto function coordinator 550. In some embodiments, function coordinator550 may be configured to manage terminal conditions and/or exceptions tofunction processing managed by distributed storage nodes. For example,function coordinator 550 may identify the plurality of storage nodeswith target symbols and/or the storage node containing the first symbolto initiate a start terminal condition by sending a serial functionprocessing request. Each storage node in the sequence of symbols maythen process the subunits in their symbol, identify the next symbol andstorage node, and pass a serial function processing request with anintermediate context to the next storage node.

In some embodiments, function coordinator 550 may include a plurality ofhardware and/or software modules configured to use processor 514 andmemory 516 to handle or manage defined operations of functioncoordinator 550. For example, function coordinator 550 may include adistributed function handler 552, a reduce-function processor 554, and aterminal result handler 556. In some embodiments, function coordinator550 may include or have access to function processor 536, incompletesubunit processor 560, and/or symbol recovery engine 570.

Distributed function handler 552 may include functions, parameters,and/or logic for managing communication with function processors in thestorage nodes. For example, distributed function handler 552 may use thetarget data unit to identify the storage nodes containing target symbolsfrom data unit/symbol map 582. Based on storage node identifiersreturned from the data unit/symbol map 582, request or command messagesmay be addressed to the relevant storage nodes. In some embodiments,distributed function handler 552 may include function processing workqueue to organize function processing tasks to be completed for a targetdata set. For example, function definition 522.1 may determine whethersymbols may be processed serially, in parallel, and/or in parallelbatches. Distributed function handler 552 may include logic forformatting and addressing function processing requests to the storagenodes. For example, distributed function handler 552 may include asymbol identifier for the target symbol, a function identifier (orfunction definition), and a prior intermediate context or seedintermediate context, if applicable, in the function processing request.In some embodiments, the function processing request may include acontext destination indicator, such as the storage node identifier forthe next symbol in a symbol order. In some embodiments, the response tothe function processing request, including the intermediate context, maybe a return or response message to function coordinator 550.

In some embodiments, distributed function handler 552 may also determineseed contexts and/or intermediate contexts passed from one data unit toanother data unit for a target data set including a plurality of dataunits. For example, a target data set may include a plurality of dataunits and each data unit may be handled according to the distributedfunction processing operations of function coordinator 550. One or moredata units in the target data set may receive a seed context value thatoperates in the place of an intermediate context for the first symbol orsymbols of a data unit. Similarly, the final intermediate context of onedata unit may be passed as a seed context to a next data unit, where theplurality of data units have a data unit order in the data set and theprocessing functions from one data unit are contingent on the results ofthe prior data unit.

Reduce-function processor 554 may include functions, parameters, and/orlogic for processing a plurality of intermediate contexts to generate afinal result, such as a reduce-function result, for return to the clientsystem. For example, reduce-function processor 554 may aggregate resultvalues received in the intermediate contexts from all of the distributedfunction processing requests to the storage nodes. In some embodiments,reduce-function processor 554 may include a context assembler 554.1 thatuses the intermediate contexts received by distributed function handler552 to provide inputs to reduce-function processor 554. In someembodiments, context assembler 554.1 may extract processing functionresults from each intermediate context and organize them according to asymbol order and/or a processing order defined by function definition522.1 for reduce-function processor 554. For example, the plurality ofintermediate contexts may be arranged in the order of symbols and/orsubunits or sorted according to another parameter or characteristic ofthe subunits or intermediate results.

In some embodiments, context assembler 554.1 may also identifyunprocessed subunits based on incomplete subunit portions received inthe intermediate contexts. For example, intermediate contexts fromadjacent symbols may include incomplete subunit portions that could becombined to form a complete subunit and represent a gap in the subunitorder and intermediate results for reduce-function processor 554.Function coordinator 550 may send incomplete subunit portions identifiedby context assembler 554.1 to incomplete subunit processor 560 forreassembly into completed subunits and/or resulting intermediatecontexts. These additional intermediate contexts may be returned tofunction coordinator 550 and added by context assembler 554.1 to theother intermediate contexts to fill in any gaps in intermediate results.In some embodiments, context assembler 554.1 may also include logic formapping the intermediate results to the series of subunits to remove anyrepeated results (such as redundant subunits in shingled symbols) andassure that intermediate results for each subunit have been received.

Reduce-function processor 554 may include one or more functions definedin function definition 522.1 for combining intermediate results. Forexample, intermediate results may include result values, such as counts,arrays of values, vectors, output tables or files, or other result datathat is aggregated and processed in accordance with reduce-function 554.Reduce-function processor 554 reduces the intermediate contexts and theintermediate results they contain to aggregate reduce-result data. Forexample, the result data may be smaller than the aggregate intermediatecontexts, which are in turn smaller than the subunits that wereprocessed to generate them. Reduce-function processor 554 may return thereduce-result for further processing by function coordinator 550.

Terminal result handler 556 may include functions, parameters, and/orlogic for receiving the reduce-result and/or an intermediate contextfrom a terminal symbol for a data unit or data set and formatting andreturning a final result for the data function processing. For example,the result data from reduce-function processor 554 may be formattedaccording to result formatter 522.3 and sent by client request handler522 to the client system. In some embodiments, terminal result handler556 may also identify terminal conditions for the distributed functionprocessing, even if it does not include a reduce-function, and receivean intermediate context from a terminal symbol that may be formatted asa final result by terminal result handler 556 without additionalfunction processing. In some embodiments, terminal result handler 556may also identify error conditions for function processing, such as whencontext assembler 550.1 fails to identify intermediate results for allsubunits in the target data set or symbol recovery engine 570 fails torecover a symbol that includes a subunit that cannot be reconstructedfrom other symbols. Terminal result handler 556 may provide anappropriate error message, with or without a partial result, to theclient system.

Incomplete subunit processor 560 may include a set of functions andparameters for managing incomplete subunit portions that may result fromsubunits that are split across symbols in some encoding configurations.Incomplete subunit processor 560 may not be local to all subunitportions it operates on. For example, incomplete subunit processor 560may be located in a storage controller to provide centralizedaggregation of incomplete subunit portions to construct completesubunits. As described above, function processor 536 in each storagenode may include incomplete subunit portions in the intermediatecontexts passed among storage nodes or to function coordinator 550. Insome embodiments, each storage node may include incomplete subunitprocessor 560 that may be called when an incomplete subunit portion isreceived in an intermediate context and may be combined with a localincomplete subunit portion to reconstruct a complete subunit.

Subunit aggregator 562 may include functions, parameters, and/or logicfor aggregating incomplete subunit portions into complete subunits. Forexample, subunit aggregator 562 may include logic for identifyingincomplete subunit order and subunit barrier conditions to reassembletwo or more subunit portions into a complete subunit. In someembodiments, subunit aggregator 562 may receive incomplete subunitportions from adjacent symbols where one incomplete subunit portion wasat the end of one symbol and the other incomplete subunit portion was atthe beginning of the next adjacent symbol and the two incomplete subunitportions are aggregated to form the original and complete subunit forfurther processing.

In some embodiments, incomplete subunit processor 560 may include afunction processor 564 similar to function processor 536. For example,once a complete subunit is aggregated by subunit aggregator 562, it maybe processed by function processor 564 to generate an intermediatecontext as if the subunit had been identified within a single symbol andstorage node. In some embodiments, incomplete subunit processor 560 maynot include its own function processor 564 and may instead forward thecomplete subunit to function processor 536 in a storage node or functioncoordinator.

Symbol recovery engine 570 may include functions, parameters, and/orlogic for recovering symbols after a failed decode operation byencoding/decoding engine 528. For example, storage nodes and/or storagecontrollers may include recovery process 572 for handling failed decodeoperations during decoding of erasure encoded symbols 592. In someembodiments, recovery process 572 may be triggered by a failed decodeoperation and may include a number of recovery operations, such asretries, using parity data to recover erase errors, and/or accessingredundant data stored in another location.

If and when the missing symbol is recovered by recovery process 572, thesymbol may be processed similar to other decoded symbols.

In some embodiments, symbol recovery engine 570 may include a functionprocessor 574 similar to function processor 536. For example, once asymbol is recovered by recovery process 572, it may be processed byfunction processor 574 to generate an intermediate context as if thesubunit had been identified within normally decoded symbol. In someembodiments, symbol recovery engine 570 may not include its own functionprocessor 574 and may instead forward the recovered symbol to functionprocessor 536 in a storage node or function coordinator.

Memory 516 may include additional logic and other resources (not shown)for processing data requests, such as modules for generating, queueing,and otherwise managing object or file data requests. Processing of adata request by storage interface 520 may include any number ofintermediate steps that yield at least one data request to thedistributed storage system.

FIG. 6 shows an example distributed storage system 600 that includes orinterfaces with a client application 610 for storing and processing adata unit 620 through distributed data function processing acrossstorage nodes 630.1-630.n. In some embodiments, storage nodes 630 may beconfigured similarly to storage system 500 in FIG. 5 for serialprocessing and a distributed or redundant function coordinator forhandling the client function request. In an alternate embodiment, astorage controller (not shown) may receive function request 612 and actas function coordinator for initiating distributed function processingand/or returning the final result to client application 610.

Client application 610 may include one or more user applicationsconfigured to store and retrieve data elements from storage nodes 630.In some embodiments, client application 610 may be configured to read,write, and otherwise manage data elements, such as data unit 620, usinga storage system interface API. Client application 610 may include afunction request interface 612 that enables a user to configure a datafunction request for processing data elements stored in storage nodes630. In some embodiments, function request interface 612 may enable theuser to provide, select, or otherwise define a function set, a data set,and/or a result format for a data function processing request to beexecuted by storage nodes 630. For example, a client request handler,similar to client request handler 522 in FIG. 5, may receive and parsethe data function processing request for initiating the distributedfunction processing across storage nodes 630.

In the example shown, data unit 620 is composed of subunits 622.1-622.n.For example, data unit 620 may include a message, data object, or datafile, and subunits 622 may include data segments corresponding tointernal data structures or patterns that form processing boundaries fordistributed processing, such as map-functions.

Storage nodes 630 may be configured for distributed function processingof subunits 622 stored as symbols 636.1-636.n in storage media635.1-635.n in each storage node 630. For example, storage nodes630.1-630.n may each include respective function processors 632.1-632.n,decoders 634.1-634.n, storage media 635.1-635.n, and peer communicationchannels 639.1-639.n. Symbols 636 may be stored as erasure encodedsymbols in local storage media 635. Decoders 634 may be used by storagenodes 630 to decode the erasure encoded symbols and provide the originalsymbols to function processors 632 for function processing.

Function processors 632 may process the symbols using the set offunctions provided in function request 612. For example, the set offunctions may include a subunit function for identifying a subunitstored within the decoded symbols and a serial-function for processingthe subunits to generate intermediate contexts 638.1-638.n. Storagenodes 630 may forward intermediate contexts 638 to the next storage nodeaccording to a symbol order from data unit 620. In some embodiments,storage nodes 630 may use peer communication channels 639 to sendintermediate contexts 638 directly to the next storage node. Forexample, peer communication channels 639 may use remote direct memoryaccess and/or direct message addressing through interconnect fabricbetween storage nodes 630.1 that bypass a control plane, such as astorage controller or host controller.

Intermediate contexts 638 may be passed from one storage node to alogically adjacent storage node (as determined by symbol order) to beused by function processors 632 to generate the next intermediatecontext. This process may continue for each symbol and subunitcorresponding to data unit until a terminal subunit 622.n and symbol636.n are reached. Storage node 630.n, containing the final or terminalsymbol 636.n, may use function processor 632.n to generate a finalcontext or result 660 using the same function processing that generatedthe prior intermediate contexts. In some embodiments, recognition of theterminal condition for a serial function in storage node 630.n maytrigger the final intermediate context to be post-processed to generateresult 660 and/or return result 660 to client application 610.

FIG. 7 shows another example distributed storage system 700 thatincludes or interfaces with a client application 710 for storing andprocessing a data unit 720 through distributed data function processingacross storage nodes 730.1-730.n. In some embodiments, storage nodes 730may be configured similarly to storage system 500 in FIG. 5 for parallelprocessing and a storage controller 740 for handling the client functionrequest.

Client application 710 may include one or more user applicationsconfigured to store and retrieve data elements from storage nodes 730.In some embodiments, client application 710 may be configured to read,write, and otherwise manage data elements, such as data unit 720, usinga storage system interface API. Client application 710 may include afunction request interface 712 that enables a user to configure a datafunction request for processing data elements stored in storage nodes730. In some embodiments, function request interface 712 may enable theuser to provide, select, or otherwise define a function set, a data set,and/or a result format for a data function processing request to beexecuted by storage nodes 730. For example, a client request handler,similar to client request handler 522 in FIG. 5, may receive and parsethe data function processing request at storage controller 740 forinitiating the distributed function processing across storage nodes 730.

In the example shown, data unit 720 is composed of subunits 722.1-722.n.For example, data unit 720 may include a message, data object, or datafile, and subunits 722 may include data segments corresponding tointernal data structures or patterns that form processing boundaries fordistributed processing, such as map-functions. Data unit 720 may havebeen provided to storage controller 740 for distributed storage acrossstorage nodes 730 as erasure coded symbols 736.1-736.n.

In the example shown, storage controller 740 may instantiate a number ofmodules to assist with function coordination across storage nodes 730.For example, storage controller 740 may include: a symbol map similar todata unit/symbol map 582 in FIG. 5; a function handler 744 similar todistributed function handler 552 in FIG. 5; a context aggregator 746similar to context assembler 554.1 in FIG. 5; a reduce-function 748similar to reduce-function processor 554 in FIG. 5; an incompletesubunit processor 750 similar to incomplete subunit processor 560 inFIG. 5; and a symbol recovery engine 752 similar to symbol recoveryengine 570 in FIG. 5.

Storage nodes 730 may be configured for distributed function processingof subunits 722 stored as symbols 736.1-736.n in storage media735.1-735.n in each storage node 730. For example, storage nodes730.1-730.n may each include respective function processors 732.1-732.n,decoders 734.1-734.n, and storage media 735.1-735.n. Symbols 736 may bestored as erasure encoded symbols in local storage media 735. Decoders734 may be used by storage nodes 730 to decode the erasure encodedsymbols and provide the original symbols to function processors 732 forfunction processing.

Function processors 732 may process the symbols using the set offunctions provided in function request 712. For example, the set offunctions may include a subunit function for identifying a subunitstored within the decoded symbols and a map-function for processing thesubunits to generate intermediate contexts 738.1-738.n. Storage nodes730 may return intermediate contexts 738 to storage controller 740 foraggregation and further processing. In some embodiments, storagecontroller 740 may send a prior intermediate context to the next storagenode in a processing order for use by function processors 732 ingenerating the next intermediate context.

Intermediate contexts 738 may be returned to storage controller 740 foraggregation by context aggregator 746 and application of reduce-function748. Once the intermediate contexts 738 for all symbols 736 are returnedto storage controller 740, storage controller 740 may aggregate andprocess the intermediate contexts through the reduce-function togenerate result 760. Storage controller 740 may return result 760 toclient application 710 in response to function request 712. In someembodiments, storage controller 740 may include incomplete subunitprocessor 750 and symbol recovery engine 752 for handling exceptionsthat may occur in decoding and/or function processing by storage nodes730.

FIGS. 8-11 show different encoding configurations for parsing data unitsinto subunits and partitioning date units into symbols, with varyingdegrees of alignment between subunits and symbols. These differentconfigurations may be enabled by an encoding/decoding engine, such asencoding/decoding engine 528 in FIG. 5.

As shown in FIG. 8, data unit 810 may be parsed into subunits812.1-812.n, such as based on a subunit function for a set of dataprocessing functions, to align with an encoding configuration 800.Subunits 812 may have varying subunit lengths or sizes determined bydata boundaries or structures identified by a subunit function. Inencoding configuration 800, symbols 820.1-820.n are assigned similarlyvarying sizes or lengths based on a varying encoded block length. As aresult, there is a one-to-one relationship between aligned subunits 812and symbols 820. All subunits 812 are complete data subunits. Note thatin some embodiments, subunits 812 may be defined to be a standard lengthor size, allowing subunits 812 and symbols 820 to both align and meet apredetermined encoded block size.

As shown in FIG. 9, data unit 910 may be parsed into subunits912.1-912.n, such as based on a subunit function for a set of dataprocessing functions, to align with an encoding configuration 900.Subunits 912 may have varying subunit lengths or sizes determined bydata boundaries or structures identified by a subunit function. Inencoding configuration 900, symbols 920.1-920.n are assigned apredetermined encoded block length that is at least as large as thelargest subunits 912. As a result, there is a one-to-one relationshipbetween aligned subunits 912 and symbols 920. Because not all subunits912 are as long or large as their corresponding symbols, such assubunits 912.1, 912.3, 912.4, additional data may be provided to fillthe difference between the subunit length and the symbol length and meetthe predetermined encoded block size. For example, null values oranother filler data pattern may be used to fill the unused portions922.1, 922.3, 922.4 of symbols 920.1, 920.3, 920.4. In an alternateembodiment, unused portions 922.1, 922.3, 922.4 may include the adjacentportion of the next subunit, rather than null values.

As shown in FIG. 10, data unit 1010 may be parsed into subunits1012.1-1012.n, such as based on a subunit function for a set of dataprocessing functions, to align with an encoding configuration 1000.Subunits 1012 may have varying subunit lengths or sizes determined bydata boundaries or structures identified by a subunit function. Inencoding configuration 1000, symbols 1020.1-1020.m are assigned apredetermined encoded block length that may not be aligned with subunitlengths. In the example shown, some subunits may align with symbols1020, but most are larger than the symbol size and span or aredistributed across more than one symbol. Note that a similarconfiguration is also possible where the symbol length may besubstantially larger than some subunits and a symbol may includemultiple complete subunits, as well as one or two partial subunits atthe ends. In a data unit with substantial variance in subunit sizes, itis possible to have some subunits that are smaller than the symbol size,creating the possibility of multiple complete subunits in one symbol,and some subunits that are larger than the symbol size, creating thepossibility of symbols with only partial subunits. In the example shown,symbol 1020.1 include a complete subunit 1012.1; symbol 1020.2 includesan incomplete subunit portion 1024.1, that is a first portion of subunit1012.2; symbol 1020.3 includes two incomplete subunit portions 1024.2(second portion of subunit 1012.2) and 1024.3 (first portion of subunit1012.3); symbol 1020.4 includes two incomplete subunit portions 1024.4(second portion of subunit 1012.3) and 1024.5 (first portion of subunit1012.4—the second portion of subunit 1012.4 would be in the next symbol,which is not shown); and symbol 1020.m includes an incomplete subunitportion 1024.8 (second portion of 1012.n—the first portion of subunit1012.n would be in the preceding symbol, which is not shown). Note thatin the example shown, the number of symbols m would be greater than thenumber of subunits n. In a configuration where the symbol size is largerthan the average subunit size, the number of symbols m would be lessthan or equal to the number of subunits n.

As shown in FIG. 11, data unit 1110 may be parsed into subunits1112.1-1112.n, such as based on a subunit function for a set of dataprocessing functions, to align with an encoding configuration 1100employing data shingling. Subunits 1112 may have varying subunit lengthsor sizes determined by data boundaries or structures identified by asubunit function. In encoding configuration 1100, symbols 1120.1-1120.mmay be assigned a predetermined symbol length 1128.1-1128.m that may notbe aligned with subunit lengths. In addition, symbols 1120 include anadditional overlap data portion 1126.1-1126.4 (though overlap dataportions up to 1126.m−1 would exist but are not shown) having an overlapdata portion length or size. In the example shown, the overlap lengthhas been configured or selected to equal the largest subunit length orsize to assure that each symbol includes at least one complete subunitand every subunit exists in at least one complete version in singlesymbol. Note that a similar configuration is also possible where theoverlap length is smaller than the largest subunit length and maycombine with the base symbol size to decrease the likelihood of anysubunit being available only split across symbols, while not assuringthat split subunits never occur. For example, these rare split subunitsmay be handled by an incomplete subunit processor, while most subunitswould be decoded from a single symbol in a single storage node.

In the example shown, symbol 1120.1 includes complete subunit 1112.1 andcomplete subunit 1112.2. Symbol 1120.2 starts from a partition based onpredetermined symbol length 1128.1 of symbol 1120.1 and includescomplete subunit 1112.2 (a second complete copy) and an incompletesubunit portion 1124.2, that is a first portion of subunit 1112.3.Symbol 1120.3 starts from a partition based on predetermined symbollength 1128.2 of symbol 1120.2 and includes two incomplete subunitportions 1124.3 (second portion of subunit 1112.2) and 1124.4 (firstportion of subunit 1112.4) and complete subunit 1112.3. Symbol 1120.4starts from a partition based on predetermined symbol length 1128.3 ofsymbol 1120.3 and includes two incomplete subunit portions 1124.5(second portion of subunit 1112.3) and 1124.6 (first portion of a nextsubunit, which is not shown). Symbol 1120.m starts from the partition ofthe prior symbol and includes an incomplete subunit portion 1124.7(second portion of 1012.n—the first portion of subunit 1112.n would bein the preceding symbol, which is not shown). Note that in the exampleshown, symbol 1120.m may be unnecessary, because the complete versionsubunit 1112.n would be in the prior symbol. In other configurations,the final symbol may include an overlap data portion and/or the finalsymbol may include a complete or partial subunit that may be needed forreconstructing all subunits from the symbols.

As shown in FIG. 12, the storage system 500 may be operated according toan example method of distributed processing of data functions acrossmultiple storage nodes, i.e. according to the method 1200 illustrated bythe blocks 1202-1238 of FIG. 12. In some embodiments, blocks 1222-1238may be executed in parallel with blocks 1202-1218. In some embodiments,blocks 1222-1238 may be executed in series with blocks 1202-1218, whereblock 1222 may be initiated following completion of block 1218.

At block 1202, a function processing request may be received at a firststorage node. For example, function processing may be initiated by aclient system directly or through a storage controller or other functioncoordinator. At block 1222, a corresponding function processing requestmay be received at a second storage node.

At block 1204, a prior intermediate context may be received. Forexample, an intermediate context from processing a prior symbol and/orprior data unit may be received by the first storage node. At block1224, a similar operation may occur at the second storage node.

At block 1206, an erasure coded symbol may be retrieved. For example, anerasure coded symbol corresponding to a target subunit for a data unitmay be read from a local storage medium by the first storage node. Atblock 1226, a similar operation may occur at the second storage node.

At block 1208, the erasure coded symbol may be decoded. For example, adecode engine in the first storage node may decode the erasure codedsymbol to recover the original symbol. At block 1228, a similaroperation may occur at the second storage node.

At block 1210, a subunit for processing may be identified from thedecoded symbol. For example, a subunit function may be applied toidentify a target subunit within the symbol data at the first storagenode. At block 1230, a similar operation may occur at the second storagenode.

At block 1212, the subunit may be processed through a distributedfunction. For example, the prior intermediate context and the identifiedsubunit may be processed through a map or serial function by the firststorage node. At block 1232, a similar operation may occur at the secondstorage node.

At block 1214, an intermediate context may be generated based on theoutput of the distributed function. For example, the function result ofthe distributed function may be included in an intermediate context bythe first storage node. At block 1234, a similar operation may occur atthe second storage node.

At block 1216, a destination for the intermediate context may bedetermined. For example, the storage controller initiating the functionprocessing request, a next storage node for a serial processing ofsymbols, or a client system may be determined as a destination by thefirst storage node. At block 1234, a similar operation may occur at thesecond storage node.

At block 1218, the intermediate context may be sent to the destination.For example, the first storage node may send the intermediate context tothe destination determined at block 1216. At block 1238, a similaroperation may occur at the second storage node.

As shown in FIG. 13, the storage system 500 may be operated according toan example method of handling incomplete subunit portions inintermediate contexts, i.e. according to the method 1300 illustrated bythe blocks 1302-1314 of FIG. 13.

At block 1302, intermediate contexts are received. For example, anincomplete subunit processor associated with a function coordinator or astorage node decoding a symbol containing an incomplete subunit portionmay receive one or more intermediate contexts that include incompletesubunit portions.

At block 1304, at least two incomplete subunit portions may beidentified. For example, incomplete subunit portions may be identifiedfrom multiple intermediate contexts or from at least one intermediatecontext and an incomplete subunit portion identified in the storagenode.

At block 1306, the at least two incomplete subunit portions may beaggregated into a complete subunit. For example, multiple incompletesubunit portions may be identified as portions of the same subunit andconcatenated in a portion order to form a complete subunit.

At block 1308, the complete subunits may be processed through adistributed function. For example, a function processor may be used toprocess the complete subunit as if it had been identified from a singlesymbol.

At block 1310, processing the complete subunits may generate additionalintermediate contexts. For example, processing the complete subunit maygenerate an intermediate result that may be included in an intermediatecontext.

At block 1312, destinations for the intermediate contexts generated atblock 1310 may be determined. For example, the storage controllerinitiating the function processing request, a next storage node for aserial processing of symbols, or a client system may be determined asdestinations for intermediate contexts generated from completed symbols.

At block 1314, the intermediate contexts may be sent to the destination.For example, the incomplete subunit processor or an associated functionprocessor may send the intermediate context to the destinationdetermined at block 1312.

As shown in FIG. 14, the storage system 500 may be operated according toan example method of distributed function process to return a functionresult, i.e. according to the method 1400 illustrated by the blocks1402-1428 of FIG. 14.

At block 1402, a target data set may be identified. For example, a datafunction processing request may be received that defines a target dataset for the function processing.

At block 1404, one or more data units may be determined from the targetdata set for processing. For example, the target data set may includeboundaries or conditions for identifying the data units that make up thetarget data set.

At block 1406, the symbols that correspond to the data units in thetarget data set may be determined. For example, the data units mayinclude one or more identifiers that may be used to search or index adata unit/symbol map and return a list of symbols corresponding to eachdata unit.

At block 1408, a symbol order may be determined for the symbolscorresponding to a data unit. For example, the list of symbols may beprovided in symbol order or may include indicators to place the symbolsin an appropriate order corresponding to the order their contents appearin the data unit.

At block 1410, storage nodes storing each of the symbols may bedetermined. For example, the list of symbols returned from the dataunit/symbol map may include a storage node identifier for the storagenode storing each symbol.

At block 1412, function processing requests may be sent to each storagenode that includes a target symbol. For example, a function processingrequest identifying the symbol to be processed may be addressed to eachstorage node based on their storage node identifier. The functionprocessing request may also identify the function(s) to be used and/orprovide a prior context, such as a starting context or intermediatecontext, to be used in the function processing.

At block 1414, intermediate contexts may be received from the storagenodes that were sent function processing requests. For example, theresponse message to the function processing request for each storagenode may include an intermediate context that resulted from the functionprocessing executed by that storage node.

At block 1416, intermediate contexts may be assembled into an orderedlist. For example, the intermediate contexts may be assembled in symbolorder to correspond to the order of subunits in the original data unit.

At block 1418, the intermediate contexts may be processed through aresult function. For example, a reduce function may be applied to eachintermediate context in list order and/or the aggregate intermediateresults included in the ordered intermediate contexts.

At block 1420, a function result may be determined for the original datafunction processing request. For example, the output of the resultfunction may be formatted in accordance with the requesting clientsystem.

At block 1422, the function result may be returned to the client system.For example, the formatted function result from block 1420 may be sentto the client system in response to the data function processingrequest.

At block 1424, a function set may be received for the function dataprocessing request. For example, a function set may be associated withthe target data set in advance of the function data processing request(such as when configuring the data units for storage in the storagenodes) or may be included with the function data processing request.

At block 1426, one or more starting context may be determined prior tosending one or more function processing requests. For example, a firstsymbol may receive a starting context based on seed values in thefunction data processing request or a result value or intermediatecontext from a prior data unit.

At block 1428, one or more additional intermediate contexts may be addedto the intermediate contexts received from the storage nodes. Forexample, an incomplete subunit processor may generate additionalintermediate contexts from incomplete subunit portions identified insymbols processed by the storage nodes.

As shown in FIG. 15, the storage system 500 may be operated according toan example method of recovering subunits for function processing after afailed decode process at a storage node, i.e. according to the method1500 illustrated by the blocks 1502-1518 of FIG. 15.

At block 1502, an encoded symbol may be identified from a failed decodeprocess. For example, a storage node that has failed to decode a targetsymbol may send a message and/or encoded symbol data to a symbolrecovery engine.

At block 1504, a recovery process may be executed. For example, a symbolrecovery engine may execute a series of retries, recovery techniques,and/or backup requests to recover the original (decoded) symbol data.

At block 1506, the original symbol may be recovered. For example, therecovery process at block 1504 may be successful and return the decodedsymbol.

At block 1508, a subunit for processing may be identified from thedecoded symbol. For example, a subunit function may be applied toidentify a target subunit within the symbol data.

At block 1510, the subunit may be processed through a distributedfunction. For example, the identified subunit (and an intermediatecontext, if received) may be processed through a map or serial function.

At block 1512, additional intermediate contexts may be generated basedon the output of the distributed function and the recovered symbol. Forexample, the function result of the distributed function may be includedin an intermediate context.

At block 1514, a destination for the intermediate context may bedetermined. For example, the storage controller initiating the functionprocessing request, a next storage node for a serial processing ofsymbols, or a client system may be determined as a destination.

At block 1516, the intermediate context may be sent to the destination.For example, the symbol recovery engine may send the intermediatecontext to the destination determined at block 1514.

At block 1518, an intermediate context may be received. For example, anintermediate context from processing a prior symbol and/or prior dataunit associated with the target symbol of the failed decode process maybe received by the symbol recovery engine for use at block 1510.

As shown in FIG. 16, the storage system 500 may be operated according toan example method of encoding subunits in symbols for distributedstorage, i.e. according to the method 1600 illustrated by the blocks1602-1628 of FIG. 16.

At block 1602, subunit parameters may be determined based on a subunitfunction or set of functions. For example, a subunit function may defineboundary conditions and/or other detectable data structures within adata unit that define and separate subunits.

At block 1604, one or more data units are received for storage inaccordance with the subunit function. For example, data units may bereceived from a client application in the normal course of using thatapplication to capture and store data of a type associated with thefunction set (and likely to be subject to future data functionprocessing requests).

At block 1606, a subunit configuration may be determined for a receiveddata unit based on the subunit function. For example, data units may beprocessed through the subunit function to determine where the boundariesbetween adjacent subunits in the data unit are located.

At block 1608, a symbol configuration may be selected. For example, astorage system may support multiple encoding configurations for aligningsubunits with symbols to be encoded and distributed among storage nodes.Selection of the symbol configuration may determine whether method 1600proceeds to block 1610, 1614, or 1620.

At block 1610, subunits may be mapped to symbols, where symbol size mayexceed subunit size. For example, symbol size may be selected to equalor exceed the largest subunit size in the data unit.

At block 1612, subunits may be padded to equal the predetermined symbolsize. For example, where a subunit size is smaller than thepredetermined symbol size null values or another filler pattern may beused to fill the difference and maintain the predetermined symbol size.

At block 1614, subunits may be mapped to symbols, where subunit sizeand/or symbol size may be varied to align subunits and symbols. Forexample, if subunit size or symbol size is configurable, subunits may bemapped to symbols on a one-to-one basis without wasted or redundantsymbol data.

At block 1616, each symbol size may be matched to each subunit size. Forexample, subunit sizes may be held as fixed and symbols sizes may beselected to match or subunit sizes may be selected for the functionsthat align with an encoded block size for symbols in the storage system.

At block 1618, an overlap size may be received as part of the symbolconfiguration. For example, if a shingled symbol configuration isselected at block 1608, a default or user customized overlay data sizemay be received or determined.

At block 1620, a symbol size portion may be determined. For example, anencoded block size for the storage system may be used as the defaultsymbol size portion.

At block 1622, an overlap data portion may be appended to the symbolsize portion to determine a total size of the shingled symbol. Forexample, the symbol size and the overlap data portion may determinewhere each symbol corresponding to the data unit will start and how muchoverlap there will be across symbols.

At block 1624, the data unit may be partitioned into symbols. Forexample, starting from the first subunit in the data unit and proceedinguntil all data in the subunit has been partitioned into at least onesymbol, symbols may be partitioned from the data unit in accordance withthe symbol configuration selected in 1608 and the alignment of subunitsto symbols in blocks 1610-1612, 1614-1616, and/or 1618-1622.

At block 1626, the original symbols may be encoded in to erasure encodedsymbols. For example, each symbol may be passed to a storage node forencoding and storage and recorded in a data unit/symbol map.

At block 1628, the encoded symbols may be stored in their respectivestorage nodes. For example, each storage node may receive one or moresymbols, encode them at block 1626, and write them to their localstorage medium.

As shown in FIG. 17, the storage system 500 may be operated according toan example method of distributed processing of serial data functionsacross multiple storage nodes, i.e. according to the method 1700illustrated by the blocks 1702-1724 of FIG. 17.

At block 1704, a prior intermediate context may be received. Forexample, an intermediate context from processing a prior symbol and/orprior data unit may be received by a storage node.

At block 1706, an erasure coded symbol may be retrieved. For example, anerasure coded symbol corresponding to a target subunit for a data unitmay be read from a local storage medium by the storage node.

At block 1708, the erasure coded symbol may be decoded. For example, adecode engine in the storage node may decode the erasure coded symbol torecover the original symbol.

At block 1710, a subunit for processing may be identified from thedecoded symbol. For example, a subunit function may be applied toidentify a target subunit within the symbol data at the storage node.

At block 1712, the subunit may be processed through a distributedfunction. For example, the prior intermediate context and the identifiedsubunit may be processed through a serial-function by the storage node.In some embodiments, the serial-function may include map and reducefunctions, described below with regard to block 1726 and 1728.

At block 1714, an intermediate context may be generated based on theoutput of the distributed function. For example, the function result ofthe distributed function may be included in an intermediate context bythe storage node.

At block 1716, a terminal condition may be determined. For example, thesubunit or symbol may be evaluated for whether it is the final orterminal subunit or symbol. If it is the terminal subunit or symbol, theterminal condition may be met and method 1700 may proceed to block 1718.If it is not the terminal subunit or symbol, the terminal condition isnot met and method 1700 may proceed to block 1722.

At block 1718, a result may be determined from the last intermediatecontext. For example, the intermediate context generated at block 1714may include a result value that may be determined to be the functionresult or may be further processed and/or formatted to determine thefunction result.

At block 1720, the function result may be returned to the client system.For example, the function result from block 1718 may be sent to theclient system in response to the data function processing request.

At block 1722, a next symbol and associated storage node may bedetermined. For example, the next storage node for a serial processingof symbols may be determined as a destination by the storage node.

At block 1724, the intermediate context may be sent to the next storagenode. For example, the storage node may send the intermediate context tothe destination determined at block 1722.

At block 1726, a temporary intermediate context may be generated tosupport the processing at block 1712. For example, a map-function may beapplied to the identified subunit to generate the temporary intermediatecontext.

At block 1728, the temporary intermediate context and the priorintermediate context may generate a function result. For example, areduce-function may be applied to the temporary intermediate context andthe prior intermediate context to generate the serial-function resultthat will be included in the intermediate context at block 1714.

As shown in FIG. 18, a client application 1810, such as the clientapplications 610 in FIGS. 6 and 710 in FIG. 7, may communicate with aclient request handler 1850, such as client request handler 522 in FIG.5, to provide a user interface system 1800. These example subsystems ormodules may be hosted by one or more nodes of distributed storage system1 and be executed using the processors, memories, and other hardware andsoftware components of those nodes. For example, client application 1810may be hosted on client nodes 10.1-10.n and client request handler 1850may be hosted on access nodes 20.1-20.n or storage nodes 30.1-30.40.

Client application 1810 may include a set of functions and parametersfor providing a user interface, such as a graphical user interface, orAPI for managing some aspects of distributed storage system 1, such asdata function processing. In the example shown, client application 1810includes a data unit definition module 1812 and a function requestmodule 1820.

Data unit definition module 1812 may include a set of function andparameters for enabling a user to identify and/or select variousparameters of data units, such as data objects or files, that may beprocessed using one or more data functions. For example, clientapplication 1810 may be configured to manage a specific subset of fileformats or data object types that are used by client application 1810,such as a media server that supports specific video or audio data unittypes or an predictive intelligence system that processes sensor datastored in particular CSV files or objects. In some embodiments, dataunit definition module 1812 may include a type selector 1814, a subunitselector 1816, and a symbol selector 1818.

In some embodiments, data unit definition module 1812 may be descriptivein nature, enabling the user to describe the data unit types used byclient application 1810 that may be used for data function processing.In some embodiments, data unit definition module 1812 may beproscriptive in nature, enabling the user to describe acceptable dataunit types that are enforced by client application 1810 at a policylevel to assure that data units stored to the storage system comply withthe selected data unit types to enable data function processing.

Type selector 1814 may include functions, parameters, and/or logic forselecting or identifying a data unit type. For example, a file or objecttype may be selected from a table of file or object types supported byclient application 1810 or a file or object definition utility mayenable the selection of file or data object parameters, such as filesize, headers, tagging, metadata, data structure, etc., associated witha defined file or object type.

Subunit selector 1816 may include functions, parameters, and/or logicfor selecting or identifying relevant data structures within the dataunit that may form the basis of subunits for data function processing.For example, data object containing markup language may include tagsdesignating chapters and/or paragraphs of a novel and subunit selector1816 may enable the user to identify the markup syntax relevant tosubunits for the types of data functions intended to process thosefiles. Subunits may be defined by subunit parameters that enableidentification of boundary conditions for individual subunits. In someembodiments, selected file formats and object types may be supported bypredefined subunit configurations and subunit selector 1816 may displaythe subunit parameters for the selected data type. In some embodiments,subunit selection may be supported by one or more markup and/or querylanguages that enable users to define boundary conditions within a dataunit based on contents of those data units.

Symbol selector 1818 may include functions, parameters, and/or logic forselecting or identifying a symbol configuration that is compatible withthe storage system and enables alignment with the subunits. For example,symbol selector 1818 may include varying patterns of symbol size and howthey align with subunits in an example data unit of the data unit typeand subunits selected. Symbol selector 1818 may support configuration ofsymbol size, overlap data length, and/or ranges for variable sizes andlengths. In some embodiments, symbol selector 1818 may provide a modelof data storage usage and the likelihood of incomplete subunits that mayneed to be moved in order to complete processing.

In some embodiments, data unit definition module 1812 may generate adefinition data structure, such as a configuration file or object, thatmay be used by client application 1810 and/or client request handler1850 to store and access data unit definitions for use in processingwrite requests and/or data function requests. In some embodiments, adata unit definition may be included in metadata associated with thedata unit.

Function request module 1820 may include a set of function andparameters for enabling a user to identify and/or select variousparameters for a data function request to storage system 500. Forexample, function request 1820 may select parameters to be included in adata function request sent to client request handler 1850 forprocessing. In some embodiments, function request module 1820 mayinclude a data set selector 1822, a function selector 1824, a parameterselector 1826, and a format selector 1828.

Data set selector 1822 may include functions, parameters, and/or logicfor selecting or identifying a data set including one or more dataunits. For example, data set selector 1822 may provide a utility forselecting previously stored files or data objects for data functionprocessing. In some embodiments, data set selector 1822 may indicatewhether stored data units fit a data unit definition from data unitdefinition module 1812 and their level of compatibility with datafunction requests. Data set selector 1822 may enforce that only dataunits of the same or compatible data unit type be included in a data settogether.

Function selector 1823 may include functions, parameters, and/or logicfor selecting or identifying one or more data functions or function setsfor processing the data set. For example, a selected data set mayinclude a data unit type that is compatible with a set of predefinedfunction types, such as map-reduce functions with defined subunit, map,and reduce functions. A user may select a function from among thepredefined function types compatible with the selected data set. In someembodiments, the storage system may only support a single function for agiven data unit type and the function may be identified andautomatically selected.

A data function, more specifically a distributed data function, mayinclude any data processing task or process to be executed against thesubunits of a data unit that return function results based on thecontents of the symbol data, which may include metadata tags. Apredefined data function may include a distributed data function definedby the storage system to support distributed processing at the storagenodes, which may be embodied in function definitions in the storagesystem, such as described with regard to function definition module 1852below. In some embodiments, predefined data functions may accept one ormore function parameters for customizing their use to a specific dataprocessing task. Function parameters may include argument parameters,such as ranges, thresholds, seed values, or other variables for apredefined data function that modify how data parameters or contents ofthe subunits are selected, counted, aggregated, or otherwise transformedinto a function result. In some embodiments, function parameters maysupport complex logical parameters defined through a language syntax,such as structured query language (SQL). For example, a predefined datafunction may include SQL processing support for a defined command setsupported by the contents and syntax of a data unit type. Functionselector 1824 may enable selection of the SQL function for querying thedata unit type. The SQL function type may include a default query syntaxfor a specific SQL query against the data unit type and/or may include afield for receiving a custom query based on the available command anddata target set for the data unit as a function parameter.

Parameter selector 1826 may include functions, parameters, and/or logicfor selecting function parameters for a selected function. For example,a selected function may include one or more seed values, constants,units of measurement, subfunctions for statistical values, etc. that areconfigurable through function parameters.

Format selector 1828 may include functions, parameters, and/or logic forselecting a return format for the result data from processing the datafunction request. For example, a selected function may support differentoutput formats, data structures, and/or locations for the returnedfunction data.

Client request handler 1810 may include a set of functions andparameters for receiving, parsing, and issuing further commands withinthe storage system to execute storage and data function requests. Forexample, client request handler 1810 may be configured similarly toclient request handler 522 in FIG. 5. In the example shown, clientrequest handler 1850 includes a function definition module 1852, a dataset selector 1864, and a result formatter 1870.

Function definition module 1852 may include a set of function andparameters for identifying storage and function parameters forsupporting storage node processing of data functions. For example,function definition module 1852 may maintain a lookup table or otherdata structure for cross-referencing data unit types with supportedpredefined functions and supporting symbol configurations. Functiondefinition module 1852 may be configured similarly to functiondefinition module 522.1 in FIG. 5. In some embodiments, functiondefinition module 1852 may include data unit types 1854, subunitfunctions 1856, symbol configurations 1858, default data functions 1860,and function parameters 1862.

In some embodiments, data unit type 1854 may include a set of uniqueidentifiers for data unit types that act as an index for other aspectsof a predefined function definition. For example, data unit types 1854may include standard data unit types, such as file formats and objecttypes, that are supported by the storage system. In some embodiments,custom data unit types may also be supported based on data unitdefinitions and function configurations received from client application1810.

In some embodiments, subunit functions 1856, symbol configurations 1858,default data functions 1860, and function parameters 1862 may bedetermined by data unit type 1854. Based on the data unit type, functiondefinition module 1852 may: identify a subunit function for functionprocessing from subunit functions 1856 that is compatible with thesubunit parameters of the data unit type; identify a symbolconfiguration for data unit storage from symbol configurations 1858 thatis compatible with subunit parameters of the data unit type; identify adefault data processing function (map-function, serial-function,reduce-function, etc.) for function processing from default datafunctions 1860, and identify any necessary or customizable functionparameters for function processing from function parameters 1862. Thesefunctions and parameters may be used by other subsystems, such asencoding/decoding engines, function processors, function coordinators,etc., to complete storage and function processing tasks.

Data set selector 1864 may be configured similarly to data set selector522.2 in FIG. 5. For example, data set selector 1864 may identify atarget data set from a data function request received from clientapplication 1810. In some embodiments, data set selector 1864 mayidentify the data unit type from the selected data set and theidentified data unit type may be used to index the function definitionsin function definition module 1852. In the example shown, data setselector 1864 may also include data type verification 1866 for verifyingthat the selected data set includes a data type that is compatible withat least one function definition and/or function parameters included inthe data function request.

Result formatter 1870 may be configured similarly to result formatter522.3 in FIG. 5. Result formatter 1870 may parse any format selectionparameters received in the data function request.

As shown in FIG. 19, the user interface system 1800 and/or storagesystem 500 may be operated according to an example method of generatinga data function request, i.e. according to the method 1900 illustratedby the blocks 1902-1922 of FIG. 19.

At block 1902, a data unit type may be selected. For example, a dataunit type supported by a client application may be selected forconfiguration to be compatible with data function processing at thestorage nodes.

At block 1904, a subunit configuration may be selected for the data unittype. For example, the subunit parameters for identifying subunitboundaries may be selected or identified from the data unit type.

At block 1906, a symbol configuration may be selected for the data unittype. For example, based on the data unit type and the subunitconfiguration a preferred symbol configuration may be selected.

At block 1908, data units may be formatted in accordance with the dataunit type and subunit configuration. For example, as the clientapplication is used in production and stores relevant data, it may beformatted into data structures and syntax matching the data unit typeand subunit configuration.

At block 1910, data units are stored to the storage system. For example,the client application may issue write commands for the formatted dataunits to a storage system configured to identify the data unit type andstore the data units using the selected symbol configuration.

At block 1912, a data set may be selected for a data function request.For example, a subset of data units with a common data unit type may beselected for data function processing.

At block 1914, the data unit type of the data units in the data set maybe identified. For example, the data unit type may be included as aparameter of the data function request or be identifiable from the dataunits themselves (header, tag, naming convention, etc.) or relatedmetadata.

At block 1916, one or more predefined functions are displayed based onthe identified unit type. For example, a particular graphics file formatmay support three predefined map-reduce functions on the storage system.

At block 1918, a selected function is identified from the set ofpredefined functions. For example, a user may use an input device toselect the predefined map-reduce function of interest for the datafunction request.

At block 1920, any parameters for the selected function may be selected.For example, the selected function may include a based value orconfigurable threshold for processing the data set.

At block 1922, a data function request may be sent to the storagesystem. For example, parameters representing the selections made inblocks 1912-1920 may be populated in a client request message to theclient request handler.

As shown in FIG. 20, the user interface system 1800 and/or storagesystem 500 may be operated according to an example method of initiatingencoding and storage compatible with function processing in response toa write request, i.e. according to the method 2000 illustrated by theblocks 2002-2008 of FIG. 20.

At block 2002, a write request may be received. For example, a writerequest may be received from a client system that includes a data unitwith a data unit type compatible with a function definition in thestorage system.

At block 2004, a data unit type may be identified in response to thewrite request. For example, the write request may specify the data unittype and/or the data unit may include indicia of data unit type.

At block 2006, a symbol configuration may be selected. For example, thedata unit type may be used to identify a symbol configuration based onthe write request and/or cross-referencing the data unit type with afunction definition that includes at least one compatible symbolconfiguration.

At block 2008, encoding and storage of the data unit in the writerequest may be initiated. For example, the data unit and the symbolconfiguration may be passed to an encoding engine for partitioning,encoding, and storage distributed across a plurality of storage nodes.In some embodiments, initiating encoding and storage may includeexecution of some portion of method 1600 in FIG. 6.

As shown in FIG. 21, the user interface system 1800 and/or storagesystem 500 may be operated according to an example method of initiatingdecoding and function processing in response to a data function request,i.e. according to the method 2100 illustrated by the blocks 2102-2112 ofFIG. 20.

At block 2102, a data function request may be received. For example, adata function request may be received from a client system thatidentifies a data set with one or more data units, such as a list ofunique identifiers associated with the data units, with a data unit typecompatible with a function definition in the storage system.

At block 2104, a data unit type may be identified in response to thedata function request. For example, the data function request mayspecify the data unit type, the data units may include indicia of dataunit type, and/or the storage system may maintain metadata on storeddata units that includes data unit type.

At block 2106, a subunit function may be selected based on the data unittype. For example, the data unit type may be used to index a functiondefinition that includes a subunit function that may be executed be eachstorage node on the symbols it contains that correspond to the data unitand identify the subunits they contain.

At block 2108, a map-function may be selected based on the data unittype. For example, the data unit type may be used to index the functiondefinition that includes a map-function or similar serial and/ordistributed function that may be executed by each storage node on thesymbols it contains that correspond to the data unit.

At block 2110, a reduce-function may be selected based on the data unittype. For example, the data unit type may be used to index the functiondefinition that include a reduce-function or similar aggregator functionthat may be executed on intermediate results generated be the storagenodes.

At block 2112, decoding and function processing of the data units in thedata set for the data function request may be initiated. For example,the data unit identifiers and the function set may be passed to afunction coordinator and/or the storage nodes containing the symbolscorresponding to the data units.

While at least one exemplary embodiment has been presented in theforegoing detailed description of the technology, it should beappreciated that a vast number of variations may exist. It should alsobe appreciated that an exemplary embodiment or exemplary embodiments areexamples, and are not intended to limit the scope, applicability, orconfiguration of the technology in any way. Rather, the foregoingdetailed description will provide those skilled in the art with aconvenient road map for implementing an exemplary embodiment of thetechnology, it being understood that various modifications may be madein a function and/or arrangement of elements described in an exemplaryembodiment without departing from the scope of the technology, as setforth in the appended claims and their legal equivalents.

As will be appreciated by one of ordinary skill in the art, variousaspects of the present technology may be embodied as a system, method,or computer program product. Accordingly, some aspects of the presenttechnology may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or a combination of hardware and software aspectsthat may all generally be referred to herein as a circuit, module,system, and/or network. Furthermore, various aspects of the presenttechnology may take the form of a computer program product embodied inone or more computer-readable mediums including computer-readableprogram code embodied thereon.

Any combination of one or more computer-readable mediums may beutilized. A computer-readable medium may be a computer-readable signalmedium or a physical computer-readable storage medium. A physicalcomputer readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, crystal, polymer, electromagnetic,infrared, or semiconductor system, apparatus, or device, etc., or anysuitable combination of the foregoing. Non-limiting examples of aphysical computer-readable storage medium may include, but are notlimited to, an electrical connection including one or more wires, aportable computer diskette, a hard disk, random access memory (RAM),read-only memory (ROM), an erasable programmable read-only memory(EPROM), an electrically erasable programmable read-only memory(EEPROM), a Flash memory, an optical fiber, a compact disk read-onlymemory (CD-ROM), an optical processor, a magnetic processor, etc., orany suitable combination of the foregoing. In the context of thisdocument, a computer-readable storage medium may be any tangible mediumthat can contain or store a program or data for use by or in connectionwith an instruction execution system, apparatus, and/or device.

Computer code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to, wireless,wired, optical fiber cable, radio frequency (RF), etc., or any suitablecombination of the foregoing. Computer code for carrying out operationsfor aspects of the present technology may be written in any staticlanguage, such as the C programming language or other similarprogramming language. The computer code may execute entirely on a user'scomputing device, partly on a user's computing device, as a stand-alonesoftware package, partly on a user's computing device and partly on aremote computing device, or entirely on the remote computing device or aserver. In the latter scenario, a remote computing device may beconnected to a user's computing device through any type of network, orcommunication system, including, but not limited to, a local areanetwork (LAN) or a wide area network (WAN), Converged Network, or theconnection may be made to an external computer (e.g., through theInternet using an Internet Service Provider).

Various aspects of the present technology may be described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus, systems, and computer program products. It will be understoodthat each block of a flowchart illustration and/or a block diagram, andcombinations of blocks in a flowchart illustration and/or block diagram,can be implemented by computer program instructions. These computerprogram instructions may be provided to a processing device (processor)of a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which can execute via the processing device or otherprogrammable data processing apparatus, create means for implementingthe operations/acts specified in a flowchart and/or block(s) of a blockdiagram.

Some computer program instructions may also be stored in acomputer-readable medium that can direct a computer, other programmabledata processing apparatus, or other device(s) to operate in a particularmanner, such that the instructions stored in a computer-readable mediumto produce an article of manufacture including instructions thatimplement the operation/act specified in a flowchart and/or block(s) ofa block diagram. Some computer program instructions may also be loadedonto a computing device, other programmable data processing apparatus,or other device(s) to cause a series of operational steps to beperformed on the computing device, other programmable apparatus or otherdevice(s) to produce a computer-implemented process such that theinstructions executed by the computer or other programmable apparatusprovide one or more processes for implementing the operation(s)/act(s)specified in a flowchart and/or block(s) of a block diagram.

A flowchart and/or block diagram in the above figures may illustrate anarchitecture, functionality, and/or operation of possibleimplementations of apparatus, systems, methods, and/or computer programproducts according to various aspects of the present technology. In thisregard, a block in a flowchart or block diagram may represent a module,segment, or portion of code, which may comprise one or more executableinstructions for implementing one or more specified logical functions.It should also be noted that, in some alternative aspects, somefunctions noted in a block may occur out of an order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or blocks may at times be executedin a reverse order, depending upon the operations involved. It will alsobe noted that a block of a block diagram and/or flowchart illustrationor a combination of blocks in a block diagram and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that may perform one or more specified operations or acts, orcombinations of special purpose hardware and computer instructions.

While one or more aspects of the present technology have beenillustrated and discussed in detail, one of ordinary skill in the artwill appreciate that modifications and/or adaptations to the variousaspects may be made without departing from the scope of the presenttechnology, as set forth in the following claims.

What is claimed is:
 1. A system, comprising: an encoding engineconfigured to: partition at least one data unit into a plurality ofsymbols, wherein each symbol of the plurality of symbols includes anoverlap data portion of an adjacent symbol of the plurality of symbols;and erasure encode the plurality of symbols into a plurality of erasureencoded symbols; a plurality of storage nodes configured to: store theplurality of erasure encoded symbols distributed among the plurality ofstorage nodes; decode the plurality of erasure encoded symbols into theplurality of symbols for the at least one data unit; and process theplurality of symbols using a map-function to generate a plurality ofintermediate contexts; and a reduce-function processor configured todetermine, responsive to receiving the plurality of intermediatecontexts from the plurality of storage nodes, a reduce-function resultusing the plurality of intermediate contexts.
 2. The system of claim 1,wherein: each storage node of the plurality of storage nodes includes atleast one storage medium configured to store at least one local erasureencoded symbol of the plurality of erasure encoded symbols; and eachstorage node of the plurality of storage nodes is further configured to:read the at least one local erasure encoded symbol from the at least onestorage medium; decode the at least one local erasure encoded symbolinto a local symbol for the at least one data unit; and process thelocal symbol using the map-function to generate at least oneintermediate context of the plurality of intermediate contexts.
 3. Thesystem of claim 2, wherein each storage node is further configured toprocess the local symbol using the map-function in parallel with atleast one other storage node of the plurality of storage nodesprocessing another local symbol using the map-function.
 4. The system ofclaim 1, wherein: the map-function is configured to process targetsubunits within each symbol of the plurality of symbols to generate anintermediate context of the plurality of intermediate contexts; targetsubunit lengths are smaller than symbol lengths, resulting in anincomplete subunit portion of each symbol; and at least a portion of theincomplete subunit portion of each symbol is included in the overlapdata portion of the adjacent symbol of the plurality of symbols.
 5. Thesystem of claim 4, wherein: the overlap data portion has a predeterminedoverlap length; a maximum target subunit length is less than thepredetermined overlap length; and, a complete version of each targetsubunit of the target subunits is included in at least one symbol of theplurality of symbols.
 6. The system of claim 4, further comprising: anincomplete target processor configured to: aggregate at least onecomplete target subunit from incomplete subunit portions, wherein theplurality of intermediate contexts includes incomplete subunit portions;process the at least one complete target subunit using the map-functionto generate at least one additional intermediate context; and add the atleast one additional intermediate context to the plurality ofintermediate contexts.
 7. The system of claim 1, wherein: thereduce-function processor is further configured to assemble theplurality of intermediate contexts into an ordered list of intermediatecontexts; the ordered list of intermediate contexts corresponds to asymbol order of the plurality of erasure encoded symbols; and a terminalsymbol in the symbol order does not include the overlap data portion. 8.The system of claim 1, further comprising: a client request handlerconfigured to: receive the map-function and the reduce-function;identify a map-reduce data set including the at least one data unit; andreturn the reduce-function result to a client system, wherein the clientsystem is not among the plurality of storage nodes.
 9. The system ofclaim 8, wherein the client request handler is further configured toreceive, from the client system, a predetermined overlap length for theoverlap data portion of each of the plurality of symbols.
 10. Acomputer-implemented method, comprising: partitioning at least one dataunit into a plurality of symbols, wherein each symbol of the pluralityof symbols includes an overlap data portion of an adjacent symbol of theplurality of symbols; erasure encoding the plurality of symbols into aplurality of erasure encoded symbols; storing the plurality of erasureencoded symbols distributed among a plurality of storage nodes;decoding, at the plurality of storage nodes, the plurality of erasureencoded symbols into the plurality of symbols for the at least one dataunit; processing, at the plurality of storage nodes, the plurality ofsymbols using a map-function to generate a plurality of intermediatecontexts; and determining, responsive to receiving the plurality ofintermediate contexts from the plurality of storage nodes, areduce-function result using the plurality of intermediate contexts. 11.The computer-implemented method of claim 10, wherein: each storage nodeof the plurality of storage nodes includes at least one storage mediumconfigured to store at least one local erasure encoded symbol of theplurality of erasure encoded symbols; decoding the plurality of erasureencoded symbols includes decoding, at each storage node of the pluralityof storage nodes, the at least one local erasure encoded symbol into alocal symbol for the at least one data unit; and processing theplurality of symbols includes processing, at each storage node of theplurality of storage nodes, the local symbol using the map-function togenerate at least one intermediate context of the plurality ofintermediate contexts.
 12. The computer-implemented method of claim 11,wherein processing, at each storage node of the plurality of storagenodes, the local symbol using the map-function is executed in parallelwith at least one other storage node of the plurality of storage nodesprocessing another local symbol using the map-function.
 13. Thecomputer-implemented method of claim 10, wherein: processing theplurality of symbols includes processing target subunits within eachsymbol of the plurality of symbols to generate at least one intermediatecontext of the plurality of intermediate contexts; the target subunitshave subunit lengths that are smaller than symbol lengths of theplurality of symbols, resulting in an incomplete subunit portion of eachsymbol; and at least a portion of the incomplete subunit portion of eachsymbol is included in the overlap data portion of the adjacent symbol ofthe plurality of symbols.
 14. The computer-implemented method of claim13, wherein: the overlap data portion has a predetermined overlaplength; a maximum target subunit length is less than the predeterminedoverlap length; and a complete version of each target subunit of thetarget subunits is included in at least one symbol of the plurality ofsymbols.
 15. The computer-implemented method of claim 13, furthercomprising: aggregating at least one complete target subunit fromincomplete subunit portions, wherein the plurality of intermediatecontexts includes incomplete subunit portions; processing the at leastone complete target subunit using the map-function to generate at leastone additional intermediate context; and adding the at least oneadditional intermediate context to the plurality of intermediatecontexts.
 16. The computer-implemented method of claim 10, furthercomprising: assembling the plurality of intermediate contexts into anordered list of intermediate contexts, wherein: the ordered list ofintermediate contexts corresponds to a symbol order of the plurality oferasure encoded symbols; and a terminal symbol in the symbol order doesnot include the overlap data portion.
 17. The computer-implementedmethod of claim 10, further comprising: receiving the map-function andthe reduce-function; identifying a map-reduce data set including the atleast one data unit; and returning the reduce-function result to aclient system.
 18. The computer-implemented method of claim 17, furthercomprising: receiving, from the client system, a predetermined overlaplength for the overlap data portion of each of the plurality of symbols.19. A system, comprising: a plurality of storage nodes configured tostore at least one data unit in a plurality of erasure encoded symbolsdistributed among the plurality of storage nodes; means for partitioningthe at least one data unit into a plurality of symbols, wherein eachsymbol of the plurality of symbols includes an overlap data portion ofan adjacent symbol of the plurality of symbols; means for erasureencoding the plurality of symbols into the plurality of erasure encodedsymbols; means for storing the plurality of erasure encoded symbolsdistributed among the plurality of storage nodes; means for decoding, atthe plurality of storage nodes, the plurality of erasure encoded symbolsinto the plurality of symbols for the at least one data unit; means forprocessing, at the plurality of storage nodes, the plurality of symbolsusing a map-function to generate a plurality of intermediate contexts;and means for determining, responsive to receiving the plurality ofintermediate contexts from the plurality of storage nodes, areduce-function result using the plurality of intermediate contexts. 20.The system of claim 19, further comprising: means for aggregatingcomplete target subunits from incomplete subunit portions, wherein theplurality of intermediate contexts includes incomplete subunit portionsfrom the means for processing the plurality of symbols; means forprocessing the complete target subunits using the map-function togenerate additional intermediate contexts; and means for adding theadditional intermediate contexts to the plurality of intermediatecontexts.